Nutritional support feeding efficiency

ABSTRACT

There is provided a method comprising: computing a target nutritional goal to reach at an end of a time interval based on a real time energy expenditure of a patient, wherein the target nutritional goal comprises a volume to be delivered (VTBD) by the end of the time interval corresponding to a target amount of energy expenditure of the patient over the time interval, computing a target feeding profile defining a target feeding rate for enteral feeding of the patient for reaching the VTBD by the end of the time interval, continuously monitoring the real time energy expenditure, adapting the target nutritional goal and corresponding VTBD to compute a maximum VTBD to reach at the end of the time interval according to the monitoring, and dynamically adapting the target feeding rate and the corresponding target feeding profile for a remaining portion of the time interval for reaching the maximum VTBD.

RELATED APPLICATIONS

This application is a Continuation-in-Part (CIP) of U.S. patentapplication Ser. No. 16/291,127 filed on Mar. 4, 2019, the contents ofwhich are incorporated herein by reference in their entirety.

This application is related to International Patent Application No.PCT/IL2017/051271 (published as WO2018/185738), titled “SYSTEMS ANDMETHODS FOR DYNAMIC CONTROL OF ENTERAL FEEDING ACCORDING TO ENERGYEXPENDITURE”, and U.S. patent application Ser. No. 15/614,641, titled“SYSTEMS AND METHODS FOR AUTOMATIC MANAGEMENT OF REFLUX DURING ENTERALFEEDING”, U.S. patent application Ser. No. 16/000,922, titled “SYSTEMSAND METHODS FOR TRACKING SPONTANEOUS BREATHING IN A MECHANICALLYVENTILATED PATIENT”, U.S. patent application Ser. No. 15/614,641, titled“SYSTEMS AND METHODS FOR AUTOMATIC MANAGEMENT OF REFLUX DURING ENTERALFEEDING”, International Patent Application No. PCT/IB2017/057702(published as WO2018/104888), titled “SYSTEMS AND METHODS FOR SENSINGLUNG FLUID”, U.S. patent application Ser. No. 15/228,115, titled “POINTOF CARE URINE ANALYZER”, by the same inventors as the presentapplication, the contents of which are incorporated herein by referencein their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to treatmentof a patient and, more specifically, but not exclusively, to systems andmethods for treatment of a patient by automated patient care.

Certain patients require assistance with feeding via an enteralapproach, require assistance with breathing, and/or require assistancewith urination, for example, patients in the intensive care unit (ICU)which may be sedated and/or intubated. Current approaches are based on amanual assessment (e.g., by a nurse, physician), and are limited intheir ability to provide optimal settings for the patient. Medicaloutcomes of patients may be improved by better control of enteralfeeding, breathing, and/or urination.

Recent studies suggest that nutritional guidelines across the majorityof intensive care units (ICUs) are not being implemented, for example,as described with reference to Bendavid I, Singer P, Theilla M, et al.(2017) Nutrition Day ICU: a 7-year worldwide prevalence study ofnutrition practice in intensive care. Clin Nutr 36:1122-1129, andHeyland D K, Schroter-Noppe D, Drover J W, Jain M, Keefe L, Dhaliwal R,Day A (2003) Nutrition support in the critical care setting: currentpractice in Canadian ICUs—opportunities for improvement? J ParenterEnteral Nutr. 27:74-83. Lack of knowledge, no technology to supportmedical staff, and general noncompliance with nutritional guidelinesresult in higher mortality and infection complications.

One of the main pitfalls is the common use of predictive equations likeHarris-Benedict equations for targeting energy prescription in criticalillness. The equations have been demonstrated by many, for example, asdescribed with reference to Zusman O, Kagan I, Bendavid I, Theilla M,Cohen J, Singer P (2018) Predictive equations Predictive Equationsversus Measured Energy Expenditure by Indirect calorimetry: ARetrospective Validation. Clin Nutr (Article in Press, and Tatucu-BabetO A, Ridley E J, Tierney A C (2015) The prevalence of underprescriptionor overprescription of energy needs in critically ill mechanicallyventilated adults as determined by indirect calorimetry: a systematicliterature review. JPEN J Parenteral Enteral Nutr 40.212-225, to beinaccurate in more than 50% of the cases, leading to under or overnutrition. In case of too low a target, patients will be underfed and,since the process is progressively increasing the rate ofadministration, calorie balance will reach large negative values thatare associated with increased morbidity, for example, as described withreference to Dvir D, Cohen J, Singer P (2005) Computerized energybalance and complications in critically ill patients: an observationalstudy. Clin Nutr 25:37-44.

Providing higher calories target that is needed has been found to beassociated with increased mortality, for example, as described withreference to Zusman O, Theilla M, Cohen J, Kagan I, Bendavid I, Singer P(2016) Resting energy expenditure, calorie and protein consumption incritically ill patients: a retrospective cohort study. Crit Care 20:367,resulting in recommendations by ESPEN to measure energy expenditure in arested condition [REE], for example, as described with reference toSinger P, Reintam Blaser A, M Berger M M, Alhazzani W, Calder P C,Casaer M (2018) ESPEN guideline on clinical nutrition in the intensivecare unit. Clin Nutr Epub ahead of publication.

SUMMARY OF THE INVENTION

According to a first aspect, system for automated enteral feeding of apatient, comprises: at least one processor executing a code for:monitoring a plurality of gastric reflux-related parameters and at leastone gastric reflux event while the patient is automatically enterallyfed by an enteral feeding controller according to a baseline feedingprofile including a target nutritional goal, training a classifiercomponent of a model for predicting likelihood of a future gastricreflux event according to an input of scheduled and/or predictedplurality of gastric reflux-related parameters, the classifier trainedaccording to computed correlations between the plurality of gastricreflux-related parameters and the at least one reflux event, feedingscheduled and/or predicted gastric reflux-related parameters into thetrained classifier component of the model for outputting risk oflikelihood of a future gastric reflux event, and computing, by themodel, an adjustment to the baseline feeding profile for reducinglikelihood of the future gastric reflux event and for meeting the targetnutritional goal.

In a further implementation form of the first aspect, the plurality ofreflux-related parameters are members selected from the group consistingof: time of day of the reflux event, enteral feeding rate during thereflux event, patient location change prior to the reflux event, andmedication administered prior to the reflux event.

In a further implementation form of the first aspect, the patientlocation change is detected by a member selected from the groupconsisting of: scheduled event requiring patient location changeextracted from an electronic health record (EHR) of the patient, ananalysis of images captured by a camera monitoring the patient, and ananalysis of inertial signals outputted by inertial sensors.

In a further implementation form of the first aspect, the monitoring isperformed over a time interval for which a risk of likelihood of thefuture reflux event was previously predicted and the adjustment to thebaseline feeding profile was previously computed, the training isperformed for the time interval for updating the trained classifier, andthe feeding is performed based on the updated trained classifier foroutputting an a new and/or updated risk of likelihood of the futurereflux event, and the adjustment is computed based on the new and/orupdated risk.

In a further implementation form of the first aspect, the adjustment tothe baseline feeding profile comprises an adjustment to a baselinefeeding rate delivered by a pump by adjusting at least one member of thegroup consisting of: a stroke rate of the pump, and a stroke amplitudeof the pump.

In a further implementation form of the first aspect, furthercomprising: iterating the monitoring, the training, the feeding, and thecomputing, wherein: monitoring is for accumulating data indicative ofthe plurality of reflux-related parameters and at least one refluxevent, training is for dynamically updating the trained classifier basedon the accumulated data, feeding is iterated for previously processedand/or new scheduled and/or predicted reflux-related parameters, and thecomputing the adjustment is dynamically performed according todynamically predicted likelihood of future reflux event.

In a further implementation form of the first aspect, the at least onereflux event is associated with a plurality of reflux-event parameters,and the classifier is trained for prediction of likelihood of the futurereflux event based on computed correlations between the plurality ofreflux-related parameters and the plurality of reflux-event parameters.

In a further implementation form of the first aspect, the at least onereflux event is defined as a requirement of the plurality ofreflux-event parameters.

In a further implementation form of the first aspect, the plurality ofreflux-event parameters are members selected from the group consistingof: reflux duration, reflux amount.

In a further implementation form of the first aspect, when the risk oflikelihood of the future reflux event denotes a likely occurrence of thefuture reflux event, the adjustment comprises a reduction in feedingrate, and when the risk of likelihood of the future reflux event denotesan unlikely occurrence of the future reflux event, the adjustmentcomprises an increase in feeding rate.

In a further implementation form of the first aspect, the increase infeeding rate is limited by a maximal feeding rate computed according toa risk of likelihood of future reflux event below a requirement for noscheduled and/or predicted reflux-related parameters.

In a further implementation form of the first aspect, the reduction andincrease in feeding rate are proportion to the risk of likelihood of thefuture reflux event.

In a further implementation form of the first aspect, the reduction andincrease in feeding rate are performed in constant predefined amounts.

In a further implementation form of the first aspect, the reduction andincrease in feeding rate are computed according to a set of rules basedon the computed the risk of likelihood of the future reflux event.

In a further implementation form of the first aspect, furthercomprising: detecting a reflux event by the monitoring while the patientis enterally fed according to a feeding rate of the baseline feedingprofile, pausing the enteral feeding by the enteral feeding controllerfor a pause time interval, adjusting the baseline feeding profile byreducing the feeding rate, and resuming the enteral feeding after thepause time interval and the reduced feeding rate.

In a further implementation form of the first aspect, furthercomprising: updating the training of the classifier according tocomputed correlations between the plurality of reflux-related parametersassociated with the detected reflux event, and the detected refluxevent, re-outputting risk of likelihood of the future reflux event,wherein the feeding rate is reduced according to the re-outputted risk.

In a further implementation form of the first aspect, the baselinefeeding profile is defined over a time interval, and the targetnutritional goal denotes an accumulation of enteral feeding parametersto reach at an end of the time interval.

In a further implementation form of the first aspect, furthercomprising: computing at the end of the time interval, a nutritionaldifference between the accumulation of enteral feeding parameters andthe target nutritional goal, and generating instructions for parenteralfeeding of the patient according to the difference. In a furtherimplementation form of the first aspect, the reflux-related parametersand the at least one reflux event are time stamped, and wherein thecorrelations are computed between the plurality of reflux-relatedparameters and the at least one reflux event having time stamps fallingwithin a common time window.

In a further implementation form of the first aspect, the correlationsare iteratively computed by sliding the common time window.

In a further implementation form of the first aspect, the common timewindow is about 15 minutes.

In a further implementation form of the first aspect, the plurality ofreflux-related parameters denote a time within a repeating physiologicalcycle, the correlation is performed between the at least one refluxevent and the time within the repeating physiological cycle, and therisk of likelihood of future reflux event is based on a current timewith respect to the repeating physiological cycle.

In a further implementation form of the first aspect, the adjustment tothe baseline feeding profile includes an adjustment of at least one of:water and medication for enteral delivery, at a defined time of day.

According to a second aspect, a system for automated patient care,comprises: at least one processor executing a code for: monitoring, overa monitoring interval, a plurality of patient-related parameters, aplurality of enteral delivered substances, and a plurality of gastricreflux-event parameters obtained while the patient is automaticallyenteral fed by an enteral feeding controller according to a baselinefeeding profile including a target nutritional goal, creating a trainingdataset by computing a plurality of feature vectors each associated withan indication of time during the monitoring interval, each featurevector storing the plurality of patient-related parameters, theplurality of enteral delivered substances, and the plurality of gastricreflux-event parameters, training a model adapted to receive currentpatient-related parameters and output instructions for adjustment of theenteral delivered substances for reducing likelihood of a future gastricreflux event, the model trained according to the training dataset basedon computed correlations between the plurality of patient-relatedparameters, the plurality of enteral delivered substances, and theplurality of gastric reflux-event parameters, and feeding currentpatient-related parameters into the trained model for outputtinginstructions for adjustment of the enteral delivered substances forreducing likelihood of a future reflux event.

In a further implementation form of the second aspect, the plurality ofpatient-related parameters are selected from the group consisting of:patient demographics, patient age, patient gender, current patientmedical diagnosis, past patient medical history, current patient signsand/or symptoms, patient vital signs, patient urine data, patientcalorimetry data, enteral feeding rate, patient location changes, bloodtest values, urinalysis test values, urine output, lung functionparameter values, lung fluid level, enteral administration of a bolus,and SpO2.

In a further implementation form of the second aspect, the enteraldelivered substances are selected from the group consisting of: enteralfeeding formula, water, and medication.

In a further implementation form of the second aspect, the reflux-eventparameters are selected from the group consisting of: time of day of thereflux event, volume of reflux, intensity of reflux, duration of reflux,weight of reflux.

In a further implementation form of the second aspect, the adjustmentcomprises entering a medication phase when administration of medicationis indicated by halting feeding for a predefined time interval forreducing likelihood of reflux.

In a further implementation form of the second aspect, the monitoring,the creating, and the training are iteratively performed for the timeinterval during which the enteral delivered substances are adjusted.

According to a third aspect, a system for generating instructions foradjustment of at least one of a mechanical ventilator and fluid balanceof a patient, comprises: at least one hardware processor executing acode for: monitoring, over a monitoring interval, output of a pluralityof sensors located on a feeding tube positioned for enteral feeding ofthe patient, a plurality of ventilation-related parameters denotingadjustable settings of the mechanical ventilator, and a plurality offluid-related parameters denoting adjustment of the fluid balance of thepatient, obtained while the patient is automatically enteral fed via thefeeding tube, creating a training dataset by computing a plurality offeature vectors each associated with an indication of time during themonitoring interval, each feature vector storing features computed fromthe output of the plurality of sensors located on the feeding tube, theplurality of ventilation-related parameters, and the plurality offluid-related parameters, training a model adapted to receive currentoutputs of the plurality of sensors located on the feeding tube andoutput instructions for adjustment of at least one of the plurality ofventilation-related parameters of the mechanical ventilator thatautomatically ventilates the patient and the plurality of fluid-relatedparameters denoting adjustment of a fluid balance of the patient, themodel trained according to the training dataset based on computedcorrelations between the output of the plurality of sensors located onthe feeding tube, the plurality of ventilation-related parameters, andthe plurality of fluid-related parameters, and feeding current outputsof the plurality of sensors located on the feeding tube into the trainedmodel for outputting instructions for adjustment of the plurality ofventilation-related parameters of the mechanical ventilator, andadjustment of the plurality of fluid-related parameters for fluidbalance of the patient for obtaining at least one member of the groupconsisting of: at least one target patient-breathing parameter and atleast one target patient-fluid parameter.

In a further implementation form of the third aspect, the monitoringfurther comprises monitoring at least one patient-breathing parameterand at least one patient-fluid parameter, wherein the feature vector ofthe training dataset further includes features computed from the atleast one patient-breathing parameter and at least one patient-fluidparameter, wherein the model is trained to receive current at least onepatient-breathing parameter and at least one patient-fluid parameterbased on computed correlations between the at least onepatient-breathing parameter, the at least one patient-fluid parameter,the output of the plurality of sensors located on the feeding tube, theplurality of ventilation-related parameters, and the plurality offluid-related parameters, wherein the feeding comprise feeding currentvalues of the at least one patient-breathing parameter and the at leastone patient-fluid parameter.

In a further implementation form of the third aspect, the at least onepatient-breathing parameter is selected from the group consisting of:SpO2, impedance sensors output, wherein the at least one patient-fluidparameter is selected from the group consisting of: administration ofdiuretic medication, administration of antidiuretic medication, amountof urine outputted, time of urine output, concentration of urine output,and amount of fluid in lungs.

In a further implementation form of the third aspect, the at least onetarget patient-breathing parameter is selected from the group consistingof: SpO2, impedance sensors output, wherein the at least one targetpatient-fluid parameter is selected from the group consisting of: amountof urine outputted over a time interval, concentration of urine output,and amount of fluid in lungs.

In a further implementation form of the third aspect, the output of theplurality of sensors comprises at least one feature computed based onthe output of the plurality of sensors, the at least one featureselected from the group consisting of: estimate of amount of fluid in atleast one lung of the patient, and estimate of spontaneous diaphragmmovement of the patient.

In a further implementation form of the third aspect, the plurality offluid-related parameters are selected from the group consisting of:administration of diuretic medication, administration of antidiureticmedication, administration of intravenous fluid administration, amountof enteral fluid administration, and type of fluid being administered.

In a further implementation form of the third aspect, the monitoring,the creating, and the training are iteratively performed for the timeinterval during which the mechanical ventilator and/or the fluid balanceof the patient are being adjusted.

According to a fourth aspect, a system for automated enteral feeding ofa patient, comprises: at least one hardware processor executing a codefor: computing a target nutritional goal to reach at an end of a timeinterval based on a basal metabolic rate of a patient obtained over aportion of the time interval, wherein the target nutritional goalcomprises a volume to be delivered (VTBD) by the end of the timeinterval corresponding to a target amount of energy expenditure of thepatient over the time interval computed according to the basal metabolicrate, computing a target feeding profile defining a target feeding ratefor enteral feeding of the patient by an automated enteral feedingdevice for reaching the VTBD by the end of the time interval,continuously monitoring the real time energy expenditure of the patientover the time interval based on continuous measurements of the basalmetabolic rate, dynamically adapting the target nutritional goal andcorresponding VTBD to compute a maximum VTBD (max VTBD) to reach at theend of the time interval according to dynamic adaptations of themonitored real time energy expenditure of the patient, dynamicallyadapting the target feeding rate and the corresponding target feedingprofile for a remaining portion of the time interval for reaching thedynamically adjusted max VTBD by the end of the time interval, whereinthe target feeding profile tracks changes of the basal metabolic rate.

In a further implementation form of the fourth aspect, a feeding ratefor reaching the maximal VTBD is computed as about 1.2*the feeding ratefor reaching the VTBD.

According to a fifth aspect, a system for automated enteral feeding of apatient, comprises: at least one hardware processor executing a codefor: computing a target feeding profile denoting a target enteralfeeding of the patient by an automated enteral feeding device forreaching a target nutritional goal at an end of a time interval,defining a baseline feeding profile of a patient by matching to thetarget feeding profile, wherein the patient is automatically fed by theautomated enteral feeding device according to the baseline feedingprofile for reaching the target nutritional goal, pausing or slowingdown the enteral feeding by the automated enteral feeding device for atleast one pause time interval, wherein a feeding deficiency is formedbetween the target feeding profile and the baseline feeding profileduring each pause time interval; and adjusting the baseline feedingprofile is to a higher feeding rate that is higher than a feeding rateof the corresponding target feeding profile to compensate the feedingdeficit for reaching the target nutritional goal at the end of the timeinterval.

In a further implementation form of the fifth aspect, the higher feedingrate is a defined maximal feeding rate.

In a further implementation form of the fifth aspect, the maximalfeeding rate is selected according to likelihood of the patientrefluxing the enteral feeding being below a threshold.

In a further implementation form of the fifth aspect, the maximalfeeding rate is computed at about 1.75*a feeding rate defined by thetarget feeding profile.

In a further implementation form of the fifth aspect, further comprisinga code for: detecting when the gap between the target feeding profileand the baseline feeding profile has closed, and reducing the baselinefeeding profile to match the target feeding profile.

In a further implementation form of the fifth aspect, further comprisinga code for: dynamically adjusting the target nutritional goal,dynamically adjusting the target feeding profile for reaching thedynamically adjusted target nutritional goal at the end of the timeinterval, and dynamically matching the baseline feeding profile to theadjusted target feeding profile for reaching the dynamically adjustedtarget nutritional goal.

In a further implementation form of the fifth aspect, the targetnutritional goal is dynamically adjusted according to dynamic values ofa computed resting energy expenditure (REE) of the patient, wherein thetarget nutritional goal is matched within a tolerance range to energyrequirements of the patient determined according the REE.

In a further implementation form of the fifth aspect, the adjustment tothe baseline feeding profile comprises an adjustment to the baselinefeeding profile dynamically matched to the adjusted target feedingprofile.

In a further implementation form of the fifth aspect, further comprisinga code for: presenting within an interactive graphical user interface(GUI) on a display, a first curve denoting the target feeding profile, asecond curve denoting the baseline feeding profile with dynamicadjustments, and marking a zone indicative of the gap of feedingdeficiency formed between the target feeding profile and the baselinefeeding profile.

In a further implementation form of the fifth aspect, further comprisinga code for automatically detecting location of a feeding tube within atarget feeding zone, and triggering the automatic enteral feeding by theautomated enteral feeding device in response to the detected location ofthe feeding tube at the target feeding zone.

In a further implementation form of the fifth aspect, the targetnutritional goal is computed match a resting energy expenditure (REE) ofthe patient within a tolerance range, the REE computed based on CO₂production rate and/or O₂ consumption rate estimates of the patient madeby measurements of at least one sensor.

In a further implementation form of the fifth aspect, the targetnutritional goal excludes hidden calories in medications prescribed tothe patient.

In a further implementation form of the fifth aspect, further comprisinga code for presenting an interactive GUI for selection of at least oneof a plurality of available feedings compositions currently in stockthat most closely match the target nutritional goal.

In a further implementation form of the fifth aspect, the target feedingprofile is computed per respective time interval of a plurality of timeintervals as an increasing percentage of a maximal value of the targetnutritional goal at the end of each respective time interval, wherein ateach subsequent time interval the percentage is increased until themaximal value is met, wherein the target nutritional goal is set to themaximal value for additional subsequent time intervals.

In a further implementation form of the fifth aspect, further comprisinga code for presenting within an interactive GUI, a dashboard presentingan indication of deviation from a target zone of a real time value of atleast one of: urine output, metabolism, gastric reflux event, REE usedto compute the target feeding profile, and gastro residual volume (GRV).

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a flowchart of a method for treating a patient by automatedadjustment of a baseline feeding rate for enteral feeding according tolikelihood of future reflux event(s), in accordance with someembodiments of the present invention;

FIG. 2 is a block diagram of components of a system for treating apatient by automated adjustment of a baseline feeding rate for enteralfeeding according to likelihood of future reflux event(s), in accordancewith some embodiments of the present invention;

FIG. 2A is a graph depicting reflux-related parameters and reflux eventsoccurring over a time interval for computing correlations thereof, inaccordance with some embodiments of the present invention;

FIG. 2B is a schematic of exemplary dataflow for automated adjustment ofenteral feeding and/or other parameters for patient management, inaccordance with some embodiments of the present invention;

FIG. 2C is a schematic of an environmental perspective of a patientbeing fed by automatic adjustment of enteral feeding according tocomputed correlations for reduction of reflux, in accordance with someembodiments of the present invention;

FIG. 3A is a graph depicting an example of adjusting a baseline feedingprofile, in accordance with some embodiments of the present invention;

FIG. 3B is a depicting the process of adjusting the baseline feedingprofile to reach a target nutritional goal, in accordance with someembodiments of the present invention;

FIG. 4 is equations for computing an estimation of an amount of enteralfeeding lost due to reflux and/or a GRV procedure, for compensating byadjustment of the baseline feeding rate, in accordance with someembodiments of the present invention;

FIG. 5 is a graph that presents a reflux event denoted y[m] that isassociated with reflux-related parameters denoted x[n] in a rangedenoted a[n,m] and b[n,m], where it is assumed that N reflux events arepresent and M possible reflux-related parameters are considered, inaccordance with some embodiments of the present invention;

FIG. 6 is a normal distribution of the accumulated reflux-relatedparameters associated with reflux events, in accordance with someembodiments of the present invention;

FIG. 7 is a depicting resulting value of each of the M calculated χ²[m]compared with the ref value χ² ^(ref) taken from a standard χ² tableunder the desired confidence level P and the number of degrees offreedom N−1, in accordance with some embodiments of the presentinvention;

FIG. 8 is a flowchart of an exemplary method for generating instructionsfor treating a patient by automated enteral feeding controlled by atrained model, in accordance with some embodiments of the presentinvention;

FIG. 9 is a flowchart of an exemplary method for generating instructionsfor treating a patient by adjustment of a ventilator and/or fluidbalance of a patient according to instructions based on output of atrained model, in accordance with some embodiments of the presentinvention;

FIG. 10 is a flowchart of an exemplary method for dynamic adjustment ofthe baseline feeding for meeting a target nutritional requirement inview of pauses in feeding and/or dynamic changes to the targetnutritional requirement, in accordance with some embodiments of thepresent invention;

FIGS. 11A-G are exemplary graphs of the target feeding profile and thebaseline feeding profile over a time interval of 24 hours, to helpunderstand the method described with reference to FIG. 10;

FIG. 12 is a graph presenting experimental results of an experimentperformed by the Inventors for assessing ability of a system thatautomatically enterally feeds a patient to meet a target nutritionalfeeding requirement in view of reflux events and/or pauses in theenteral feeding, in accordance with some embodiments of the presentinvention;

FIG. 13, which is a schematic of an exemplary GUI presenting an overallpatient status, in accordance with some embodiments of the presentinvention;

FIG. 14 is a graph of exemplary typical CO₂ production rates, inaccordance with some embodiments of the present invention;

FIG. 15A-B includes graphs depicting exemplary nutritional daily chartsfor a patient, summarizing exemplary important nutritional status of thepatient, in accordance with some embodiments of the present invention;

FIG. 15C is a graph depicting an example of a halt in feeding, which iscompensated for by automatically increasing the feeding rate, inaccordance with some embodiments of the present invention;

FIG. 15D is a graph depicting an example of a change in the targetfeeding profile, in accordance with some embodiments of the presentinvention;

FIG. 16 is a flowchart of an exemplary method of operational flow of anautomated patient feeding device, in accordance with some embodiments ofthe present invention; and

FIG. 17, which is a schematic that graphically depicts correlationsbetween parameters and reflux events, in accordance with someembodiments of the present invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to treatmentof a patient and, more specifically, but not exclusively, to systems andmethods for treatment of a patient by automated patient care.

As used herein, the terms patient-related parameters and reflux-relatedparameters may sometimes be interchanged, for example, when clinicalparameters measured for the patient are correlated with risk of reflux.

As used herein, the terms reflux-event parameters and at least onereflux event may sometimes be interchanged, for example, the refluxevent may be defined according to one or more reflux-event parameters.

As used herein, the term reflux-related parameters refers to gastricreflux-related parameters, and/or the term reflux event refers togastric reflux event.

An aspect of some embodiments of the present invention (sometimesreferred to herein as artificial intelligence (AI), adaptive system,machine learning relating) to systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) for personalized treating (and/or for generatinginstructions for treating) a patient by automated adjustment of anenteral feeding rate provided by an enteral feeding controller and/orventilation assistance such as controlled suction. The adjustment ispersonalized for the patient, based on code of a model (e.g., machinelearning code, for example, a classifier component) that learns the mostoptimal way to enteral feed the patient (e.g., to reach a targetnutritional goal) while reducing risk of reflux events. The adjustmentin rate is performed automatically by the model that learns correlationsbetween reflux-related parameters and risk of reflux event (see FIG.2B), and adjusts the feeding rate to reduce risk of reflux event. Themodel (e.g., statistical classifier component of the model) is trainedfor the current patient, i.e., trained differently for each patientaccording to data collected for the respective patient.

An aspect of some embodiments of the present invention relate tosystems, methods, apparatus, and/or code instructions for automatedenteral feeding of a patient by an automated enteral feeding device. Atarget feeding profile denoting a target enteral feeding of the patientfor reaching a target nutritional goal at an end of a time interval iscomputed, for example, based on real time basal metabolic rate (alsoreferred to as basal rate). The basal metabolic rate indicates the realtime energy expenditure of the patient (e.g., REE), which may becomputed based on sensor measurements over a short time interval (e.g.,5-15 minutes, or other values) which is extrapolated to the end of thetime interval (e.g., at the end of 6, 12, 24, hours, or other values).The target feeding rate is computed based on the 24 hour (used as a nonlimiting example) target, for example, a feeding plan for the next 24hours. The predicted 24 hour energy expenditure target to reachcorresponds to a volume of an enteral feeding formulation selected toenteral feed the patient. The target volume of the enteral feedingformulation to provide by the 24 hour time target may sometimes bereferred to herein as volume to be delivered (VTBD). A baseline feedingprofile of a patient is defined by matching to the target feedingprofile. The baseline feeding profile may include a baseline feedingrate matched to the target feeding rate to reach VTBD by the end of the24 hour time interval. The patient is automatically fed by the automatedenteral feeding device according to the baseline feeding profile forreaching the target nutritional goal. The enteral feeding may be pausingor slowing down by the automated enteral feeding device for at least onepause time interval. A feeding deficiency is formed between the targetfeeding profile and the baseline feeding profile during each pause timeinterval. The baseline feeding profile is adjusted is to a higherfeeding rate (optionally a maximal feeding rate) that is higher than afeeding rate of the corresponding target feeding profile to compensatefor the feeding deficit for reaching the target nutritional goal at theend of the time interval. The maximal feeding rate compensates for thefeeding volume that would otherwise have been provided to the patient ifnot for the pause (or reduced rate) in the feeding, by providingadditional volumes of feeding formula during the remaining amount oftime. The compensation is designed to result in the same VTBD at the endof 24 hours that would otherwise have been provided if not for the pause(or reduced rate) in feeding.

An aspect of some embodiments of the present invention relate tosystems, methods, apparatus, and/or code instructions for automatedenteral feeding of a patient by an automated enteral feeding device. Atarget nutritional goal to reach at an end of a time interval iscomputed, optionally based on a basal metabolic rate (indicative of realtime energy expenditure) of the patient obtained over a short portion ofthe time interval (e.g., 5-15 minutes, or other values), for example,2000 Kcal in 24 hours based on about 20.8 Kcal basal metabolic rateand/or energy expenditure in 15 minutes. The target nutritional goalcomprises a volume to be delivered (VTBD) of a selected feedingformulation by the end of the time interval corresponding to the energyrequirement of the time interval, for example, for a formula where 1milliliter (ml) provides 2 Kcal, VTBD is 2000 Kcal/2 ml per Kcal=1000ml. The VTBD of the feeding formulation corresponds to a target amountof energy expenditure of the patient over the time interval computedaccording to the real time energy expenditure, by extrapolating from theshort portion to the whole time interval. A target feeding profiledefining a target feeding rate for enteral feeding of the patient by anautomated enteral feeding device for reaching the VTBD by the end of thetime interval is computed. For example, to reach 1000 ml by the end of24 hours the feeding rate is about 1000/24=41.7 ml/hour. The real timeenergy expenditure of the patient is continuously monitored (or aboutcontinuously over short time intervals, for example, every second,minute, 5 minutes, or other values) over the time interval, optionallybased on continuous measurements of the basal metabolic rate. The targetnutritional goal and corresponding VTBD are dynamically adapted tocompute a maximum VTBD (max VTBD) to reach at the end of the timeinterval according to dynamic adaptations of the monitored real timeenergy expenditure of the patient. For example, when the patientimproves at hour 18 of the 24 hour time interval, and the new energyexpenditure increases by 20%, a new target at the end of the timeinterval is computed to provide the patient with the extra 20% over theremaining portion of the time interval, i.e., for the remaining 6 hours.The max VTBD is computed, as initial VTBD and the additional volume overthe previous VTBD to reach the new energy target. The target feedingrate and the corresponding target feeding profile are adapted for theremaining portion of the time interval for reaching the dynamicallyadjusted max VTBD by the end of the time interval. For example, for theremaining 6 hours, the feeding rate is increased to reach the new targetof max VTBD. Effectively, the target feeding profile tracks changes ofthe basal metabolic rate.

The max VTBD may be defined by a user such as a physician in order tocompensate losses.

The feeding rates described herein may be computed, for example, interms of ml/hour, or ml/minute, or other time intervals.

The automated feeding may be paused, for example, due to a manual stopby a user, automatic stop by the device when reflux is detected, clogsin the gastric tube feeding outlet, and/or other reasons describedherein.

It is noted that the volume digested by the patient may sometimes differfrom the volume delivered to the patient. The systems, methods,apparatus, and/or code instructions described herein may refer toachieving digested volume for a patient by dynamically changing thedelivered volume according the initial baseline, as described herein.

Optionally, a target feeding profile is set for reaching a targetnutritional goal at an end of a time interval, for example, 24 hours.The baseline feeding profile, which indicates the actual feedingsprovided to the patient, is matched to the target feeding profile, whichrepresent the desired feeding goal. A gap is formed between the targetfeeding profile and the baseline feeding profile during pauses of theenteral feeding. The gap indicates a difference between the totalaccumulated feedings actually administered to the patient, and thedesired accumulated feedings. To close the gap, the feeding rate of thebaseline feeding profile is increased. When the gap has closed, thefeeding rate of the baseline feeding profile is reduced, to match thefeeding rate of the target feeding profile. When the target nutritionalgoal is dynamically adjusted (e.g., increased), before the end of thetime interval, the target feeding profile is adjusted (e.g., increased).The baseline feeding profile is adjusted (e.g., increased) to match theadjusted target feeding profile, in an attempt to reach the new targetnutritional goal by the end of the time interval.

Optionally, for a patient (e.g., hospitalized, such as in the ICU), alarge number of medical parameters are monitored (e.g., continuouslysuch as by sensor, per event such as blood test results, regularly suchas medical examination rounds by physicians and/or nurses) and may bestored in the electronic health record (EHR), for example, stored in anEHR server. The EHR accumulates past medical information and statisticsof medical parameters (e.g., variables and/or signal) that potentiallymay affect the enteral feeding policy in form of food type and/orfeeding rate. Alternatively or additionally, current medical statusindicators for example oxygen saturation SPO2 heart data, ventilationdata and patient motion (forced by care taker or spontaneous) may berecorded and stored in the EHR or other associated dataset.

The feeding rate may be increased when the risk of reflux is low, tocompensate for reduced feeding during reduced feeding rates and/orpauses, for example, in the form of feeding stops (pause) which may beautomatically triggered when reflux is detected and/or manually done bya user (e.g., care taker decision to pause the feeding for one or morereasons), to reach an optional target nutritional goal at the end of afeeding time interval. The correlations and adjustment of the feedingrate are iterated following the accumulated patient sensory data, whichreduces overall risk of reflux events while providing enteral feedingfor reaching as close as possible to the target nutritional goal.Optionally, when the patient has skin electrodes applied thereon, forexample a lung water (i.e., edema), electrode and/or limbs electrodes,cross correlation may be computed to detect the cross effects betweenfeeding parameters, water administration and/or medicationadministration and/or lung fluid.

Reflux-related parameters (e.g., time of reflux event, enteral feedingrate, patient orientation change (e.g., as detected by camera and/orinertial body strapped sensors), and administered medication) and refluxevents are monitored while the patient is being automatically enteralfed by an enteral feeding controller. The patient is enteral fedaccording to a baseline feeding profile, which includes a targetnutritional goal. The target nutritional goal is optionally defined foran end of a feeding time interval, computed as an aggregation over thefeeding time interval, for total calories and/or total volume deliveredover 24 hours. A model (e.g., classifier component) is trained forpredicting likelihood of a future reflux event according to an input ofa scheduled and/or predicted reflux-related parameter(s), for example,risk of reflux given a certain feeding rate and a scheduled patientorientation change event (e.g., due to a patient procedure), or risk ofreflux associated with an administration of a certain medication. Themodel (e.g., classifier component) is trained according to computedcorrelations between the reflux-related parameters and the refluxevent(s). Scheduled and/or predicted reflux-related events are fed intothe model (e.g., classifier component) for outputting risk of likelihoodof a future reflux event. The baseline feeding profile is adjusted forreducing likelihood of the future reflux event and for meeting thetarget nutritional goal. At least some implementations of the systems,methods, apparatus, and/or code instructions described herein include alearning and/or training model that automatically adjusts operatingparameters to obtain a best fit for the specific patient. For example,when a certain medication has been previously associated with high riskof reflux, and the medication is scheduled to be administered again inthe near future, the feeding rate may be reduced or even stopped inadvance of the administration to prevent or further reduce risk ofreflux. In another example, when a patient orientation change isscheduled (e.g., detected by an analysis of images of the patientcaptured by a camera, for example, daily changes of the patient'sbedding), the feeding rate may be stopped during the orientation change(due to risk of reflux) and increased before and/or after theorientation change (when risk of reflux is low) for compensating forlack of feeding during the pause. In another example, the model (e.g.,classifier component) learns risk of reflux during different times ofthe day (e.g., due to a physiological cycle of the patient) and adjuststhe feeding rate accordingly to maximize feeding while reducing risk ofreflux at different times of the day.

The feeding rate instructed by the enteral feeding controller (e.g.delivered by a pump) may be controlled, for example, by stroke frequencyand/or by stroke amplitude and/or by combining frequency control withamplitude control.

The monitoring, training, and adjustment are iterated over the feedingtime interval, for dynamically updating the correlations, dynamicallycomputing risk of reflux for new scheduled reflux-related parametersand/or dynamically re-computing risk for known reflux-related parameters(using the updated correlation values), and dynamically adapting thefeeding rate. The correlations values are updated for the respectivepatient based on the monitoring data, which increases accuracy ofpredicting risk of reflux-related parameters, and improves the abilityto reach a tradeoff between a faster feeding rate (to reach the targetnutritional goal) and reduction of risk of reflux events.

It is noted that the correlations may be between each reflux-relatedparameter and risk of reflux event, and/or between a combination ofmultiple reflux-related parameters and risk of reflux event. Forexample, certain individual reflux-related parameters may not besignificantly correlated with a significant risk of reflux event,however a combination of multiple such reflux-related parameters may besignificantly correlated with significant risk of reflux event.

Exemplary correlation of patient clinical parameters that arestatistically significantly correlated to risk of reflux, discovered byInventors during clinical studies using our implementations of adesigned feeding tube, for example, as described in the applicationsincorporated by reference:

-   -   Reflux versus SPO2 (e.g., measured by a pulse oximeter).    -   Reflux versus Patient position and/or movement, optionally        changes thereof (e.g., measured by camera and/or inertial        sensors)    -   Reflux versus Bolus enteral administration (e.g., initiated by        care taker decision)    -   Lungs water and/or edema versus Urine output (e.g., measured by        skin and feeding tube electrodes)    -   Reflux versus lung fluid    -   Reflux versus urine output Examples of systems and methods for        sensing lung fluid and functionality are described with        reference to International Patent Application No. IB2017/057702.

Examples of systems and methods for tracking spontaneous breathing in amechanically ventilated patient are described with reference toapplication Ser. No. 16/000,922.

Examples of systems and methods for analyzing urine are described withreference to application Ser. No. 15/228,115.

Exemplary correlations computed herein (e.g., by the computing device)may be of the following form:

-   -   let r_(i) be a reflux sequence of n elements    -   let m_(j,i) be the n measurements sequence of parameter j

Then:

$\rho^{j} = \frac{\sum\limits_{i = 1}^{i = n}\; \left( {r_{i} - \overset{\_}{\left. r \right) \cdot \left( {m_{J,\iota} - \overset{\_}{\left. m_{J} \right)}} \right.}} \right.}{\sqrt{\sum\limits_{i = 1}^{i = n}\; \left( {r_{i} - {{\overset{\_}{\left. r \right)}}^{2}{\sum\limits_{i = 1}^{i = n}\; \overset{\_}{\left( {m_{J,\iota} - \left( {\overset{\_}{\left. m_{J} \right)}}^{2} \right.} \right.}}}} \right.}}$

Then for example: if ρ^(j)<0.5 weak correlation

-   -   If ρ^(j)>0.9 strong correlation, halt feeding

The thresholds of 0.5 and 0.9 are exemplary. Other values for athreshold denoting weak and/or strong correlations may be used, forexample, threshold for weak correlation may be 0.3, 0.4, 0.6, 0.7, orother smaller, larger, or intermediate values. Threshold for strongcorrelation may be, for example, 0.7, 0.8, 0.95, or other smaller,larger, or intermediate values. The threshold differentiating betweenweak and strong may be the same value, or different values. Parametershaving strong correlations with reflux may be selected for adjustment ofentral substance delivery (e.g., formula, water, medication) forreducing risk of reflux, as described herein. For example, when thecomputed correlation is above the strong correlation threshold,instructions for halting feeding may be generated. Parameters havingweak correlations with reflux may be ignored for adjustment of entralsubstance delivery. For example, when the computed correlation is belowthe weak correlation threshold, feeding may continue. Parameters havingcorrelations between weak and strong may be analyzed, for example,according to a set of rules, by the model, based on manual input, and/orother methods. For example, when the computed correlation is between theweak and strong threshold, instructions to adapt the feeding may begenerated, for example, reduce the feeding rate without necessarilystopping feeding. The amount of reduction in rate may be proportion tothe degree of correlation, for example, linearly, exponentially,non-linearly, and/or computed by the model.

Optionally, the adjustment to the baseline feeding profile includes anadjustment of water and/or medication for enteral delivery. For example,addition of extra water and/or administration of one or moremedications. Optionally, the adjustment defines the time of day (e.g.,range) when the water and/or medication is to be delivered.

Exemplary events that may trigger a feeding pause (i.e., stop, which maybe temporary) include:

-   -   Administration of muscle relaxation medication (e.g., according        to values stored in the EHR).    -   Patient movement (e.g., as indicated by an analysis of images        captured by camera or inertial sensors).    -   Administration of a bolus (e.g., according to values stored in        the EHR).

An aspect of some embodiments of the present invention relates tosystems, methods, apparatus, and/or code instructions (i.e., stored in amemory and executable by one or more hardware processors) for treating(and/or for generating instructions for treating) a patient by automatedenteral feeding. Data elements are obtained (i.e., monitored) over amonitoring interval. The obtained data elements include patient-relatedparameters, enteral delivered substances, and/or reflux-eventparameters. The data elements are obtained while the patient isautomatically enteral fed by an enteral feeding controller, optionallyaccording to a baseline feeding profile including a target nutritionalgoal. A training dataset is created. The training dataset includesfeature vectors each associated with an indication of time during themonitoring interval when the data elements were obtained. Each featurevector stores the patient-related parameters, the enteral deliveredsubstances, and the reflux-event parameters, obtained during a timeinterval corresponding to the feature vector. A model is trained and/orgenerated based on the feature vectors of the training dataset. Themodel is adapted to receive current patient-related parameters and/orenteral delivered substances, and output instructions for adjustment ofthe enteral delivered substances. The adjustment may be for reducinglikelihood of a future reflux event. The model may be trained accordingto computed correlations between patient-related parameters, enteraldelivered substances, and/or reflux-event parameters. Currentpatient-related parameters are fed into the trained model for outputtinginstructions for adjustment of the enteral delivered substances (e.g.,water, medication, enteral formula), optionally for reducing likelihoodof a future reflux event. The enteral delivered substances are adjustedaccording to the instructions.

An aspect of some embodiments of the present invention relates tosystems, methods, apparatus, and/or code instructions (i.e., stored in amemory and executable by one or more hardware processors) for treating(and/or for generating instructions for treating) a patient byadjustment of a mechanical ventilator and/or fluid balance. Dataelements are obtained (i.e., monitored) over a monitoring interval. Theobtained data elements include output of sensors located on a feedingtube positioned for enteral feeding of the patient (e.g., as describedwith reference to application Ser. No. 16/000,922, and/or IB2017/057702), ventilation-related parameters, and/or fluid-relatedparameters. The data elements are obtained while the patient isautomatically enteral fed by an enteral feeding controller. A trainingdataset is created. The training dataset includes feature vectors eachassociated with an indication of time during the monitoring intervalwhen the data elements were obtained. Each feature vector stores theoutput of the sensors located on the feeding tube, ventilation-relatedparameters, and/or fluid-related parameters, obtained during a timeinterval corresponding to the feature vector. A model is trained and/orgenerated based on the feature vectors of the training dataset. Themodel is adapted to receive current outputs of the sensors located onthe feeding tube and output instructions for adjustment of themechanical ventilator that automatically ventilates the patient andand/or the fluid balance of the patient. The adjustment may be forreaching a target patient-breathing parameter (e.g., SpO2, output ofimpedance sensors) and/or target patient-fluid parameter (e.g., urineoutput over a time interval). The model may be trained according tocomputed correlations between the output of the sensors located on thefeeding tube, and/or the ventilation-related parameters, and/or thefluid-related parameters. Current outputs of the sensors located on thefeeding tube are fed into the trained model for outputting instructionsfor adjustment of mechanical ventilator and/or fluid balance of thepatient (e.g., give diuretic medication, give antidiuretic medication,give IV saline, give extra water with enteral feeding), optionally forobtaining the target patient-breathing parameter (e.g., target SpO2value, target value of output of impedance sensor(s)) and/or targetpatient-fluid parameter (e.g. target urine output over 24 hours). Themechanical ventilator and/or the fluid balance are adjusted according tothe instructions.

Optionally, the feature of obtaining the data items, the creation of thefeature vector, and the training of the model are iterated over time toupdate the model based on outcomes of the adjustment. The model is fednew values and computes new adjustments based on the updated model. Inthis manner, the model iteratively learns results of its adjustmentoutputs, for improving the adjustment, optionally for further reducingrisk of reflux.

At least some of the systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) described herein relate to the problem of treatmentof a patient by automated patient care. Most of the ICUs (and/or otherexemplary healthcare departments) are not equipped with computerizedinformation systems allowing calculation of daily and cumulative energybalance. This value may be missed if not evaluated daily, and theimportance of keeping tight calorie balance may be disregarded. Thecumulative negative energy balance may reach −10,000 kcal, therebyincreasing the risks of morbidity and mortality, for example, asdescribed with reference to Dvir D, Cohen J, Singer P (2005)Computerized energy balance and complications in critically illpatient.s: an observational study. Clin Nutr 25:37-44. Anothersubstantial risk is the rehabilitation of a patient post-discharge thatmay be prolonged substantially, for example, as described with referenceto Wischmeyer P E, San-Millan I (2015) Winning the war againstICU-acquired weakness: new innovations in nutrition and exercisephysiology. Crit Care 19:s6. At least some of the systems, methods,apparatus, and/or code instructions described herein provide a nextgeneration of feeding technology that explores the benefits ofcontinuous energy expenditure and use AI and prediction algorithms tohandle caloric and protein deficit. Communication between ICU andpost-ICU discharge units to understand nutrition regime and predictivedeficit, and an app to help the patient to recover after post-hospitaldischarge may be provided. Using the knowledge acquired in the analysisof hospital food left uneaten by patients to evaluate the energy,protein and vitamins deficits, at least some of the systems, methods,apparatus, and/or code instructions described herein may overcome thedeficits in the post ICU period. When implemented in the critical stage,at least some of the systems, methods, apparatus, and/or codeinstructions described herein may help to ensure that a patient'snutritional plan and goals are constantly measured, monitored andachieved. Wearable data may be integrated with EHR data, including datafrom sensors continuously measuring physical activity and glucose level(missing reference).

At least some of the systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) described herein improve patient safety, reducemortality, and/or decrease complications and length of stay in the ICU.

At least some of the systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) described herein may assist with one or more of thefollowing: 1. Optimal patient feeding through a combination of real-timereflux detection and prevention. 2. Energy expenditure measurement andcontinuous personalized feeding formula selection. 3. Continuousmonitoring of the enteral feeding delivery. 4. Calculations andcontinuous monitoring of supplement nutrition. 5. Automated informationflow between units.

At least some of the systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) described herein relate to the problem of treatmentof a patient by enteral feeding, optionally including water and/ormedication administration. Optionally, when special fluid administrationassociated with increased likelihood of reflux is used, for examplemedication administration, the pumping procedure may change to a“medication phase” for reducing likelihood of risk of reflux and/orreducing likelihood of risk of GRV. The Medication phase may be carriedout, for example, by terminating the feeding for a period of time aheadof the medication phase and optionally reducing the delivering, forexample, reducing the pumping rate for reducing the likelihood ofreflux.

Design and/or selection of an appropriate enteral feeding regime, whichincludes the feeding rate (e.g., calories per unit of time) and/orcomposition of the enteral feeding (e.g., mix of carbohydrates, protein,fat and/or other nutrients) affects patient survival and recovery. Thereis a tendency to increase the feeding rate in order to provide thepatient with sufficient amount of nutritional. However, on the otherhand, a feeding rate that is too high results in reflux events. Refluxincrease risk of complications for the intubated patient, for example,aspiration pneumonia resulting from reflux being aspirated into thelungs. Reducing the feeding rate to a low level may reduce risk ofreflux, but may increase the risk of the patient not receivingsufficient nutrition which is associated with increased morbidity,mortality, and/or complications. Selection of an optimal feeding profilewhich on the one hand provides correct nutrition for the patient toimprove treatment outcome, and on the other hand avoids or reduces riskof reflux is a challenging task at least due to the described tradeoffs.

Careful feeding control has been shown to be as a patient recuperationaccelerator. For example, new intensive care unit (ICU) guidelinesrecommend to administer early enteral nutrition in critically ill adultpatients when oral intake is not possible. Also, to avoid overfeeding,early full enteral nutrition shall not be used in critically illpatients but shall be prescribed within at least three days and morespecifically in the first day 30% of the suggested nutrition, second day50% third day 70% and from the fourth day on, until the full suggestedamount is reached. The suggested amount may be determined by standardpractice methods.

When following the guidelines for feeding, a preset feeding profile isdetermined and administered, where the same amount is provided allhours, every hour. The feeding rate is not adapted according to theever-changing condition of the patient. As a result, the feedingprocedure is not capable of compensating for food losses and is notcapable of minimizing adverse events, for example, feeding halt due toreflux, gastric residual volume aspiration (GRV) and/or feeding halt forpatient treatment. Standard feeding devices are no designed to reducerisk of such adverse events. Effectively, the delivered nutrition doesnot equal the digested nutrition that the patient actually absorbs, atleast due to reflux where at least some of the enteral delivered foodleaves the body of the patient (e.g., GRV).

In addition, the food consumption varies during the day due to patient'schanging condition so if the daily routine stays and the feeding profilestays the same, some parts of the day will have reflux events andpossibly massive reflux that will cause aspiration and aspirationpneumonia, because of low gastric emptying activity, and some parts ofthe day that potentially enable the caregiver to feed more will stay thesame. Standard feeding methods are do not adopting a temporal feedingprofile to better fit the patient's need when fast recuperation is thenatural goal.

At least some of the systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) described herein improve the technical field ofautomated enteral feeding of a patient for treatment thereof. Theimprovement is based on generating instructions for automated control ofthe enteral feeding by a feeding controller, which achieves an optimaltrade-off between reaching as close as possible (and/or meeting) atarget nutritional goal for increasing medical outcomes, and reducing orpreventing risk of reflux which is linked to complications such asaspiration pneumonia.

The gastric administration of enteral feeding is associated with morevomiting and reflux than parenteral nutrition, for example, as describedwith reference to Reignier J, Boisramé-Helms J, Brisard L, Lascarrou JB, Ait Hussain A, Anguel N et al. (2018) NUTRIREA-2 Trial Investigators;Clinical Research in Intensive Care and Sepsis (CRICS)group:Enteralversus parenteral early nutrition in ventilated adults with shock: arandomised, controlled, multicentre, open-label, parallel group study(NUTRIREA-2). Lancet 391:133-143. The reflux and regurgitation are afactor for increasing the risks of ventilator-associated pneumonia(VAP), and there is a constant conflict between increasing feeding ratefor reaching the nutritional goal and reducing feeding rate fordecreasing aspiration. Therefore, clinicians are cautious in theprogression of the rate of administration of enteral feeding in order todecrease the risks of reflux and vomiting and not increase the risks ofVAP. Elevating the head of the bed to a half sitting position is theonly demonstrated technique that reduces the incidence of VAP. Theassessment of gastric function using gastric residual volume (GRV) testperiodic aspiration is clinically used, but is not useful in monitoringenteral nutrition administration, for example, as described withreference to Reignier J, Mercier E, Le Gouge A, Boulain T, Desachy A,Bellec E et al. (2013) Effect of not monitoring residual gastric volumeon risk of ventilator-associated pneumonia in adults receivingmechanical ventilation and early enteral feeding: a controlledrandomized trial. JAMA 209:249-256. For an effective result of gastricevacuation, gastric residues should be released when reflux occurs, andnot in an arbitrary and pre-determined (once every 4 hours) timeframe.

Known methods include, for example, predictive equations (e.g.Harris-Benedict) to determine nutritional requirements, and manuallymeasuring gastric residual volumes (GRV) to determine safe enteralfeeding rates. However, such known methods are not patient specific, andare based on a general approach based on studies performed for a wideranging patient population. Moreover, such methods due not provide adynamic tradeoff that balances reduction in risk of reflux with the goalof providing the patient with enterally delivered nutrition to reach atarget nutritional goal. The described approach does not adapt to thespecific patient needs, i.e., the approach at least has no learningcapability, in contrast to at least some implementations of the systems,apparatus, methods, and/or code instructions described herein that havelearning capability for adapting to needs of each specific patient.

Additional details of the technical problem related to reducing risk ofreflux and/or aspiration pneumonia is described with reference toapplication Ser. No. 15/614,641. Additional details of the technicalproblem related to treatment of the patient by proper enteral feedingfor increasing medical outcomes is described with reference toInternational Patent Application No. IL2017/051271.

As discussed herein, the compliance to guidelines as well as toprescription rarely achieves the nutritional goal. Numerous reasons mayexplain this poor adherence, but the need to give the prescribed dose ofenteral feeding is not perceived as the need to administer an adequateand timely antibiotic prescription. An assessment of the patient'sdelivered feeding efficiency is automatically performed by at least someimplementations of the systems, methods, apparatus, and/or codeinstructions described herein, not manually by the nurse. Exemplaryelements that may be continuously calculated are: 1. Time lost bystopping feeding, for example, related to surgical procedure, CT or MRItests. 2. Calculating and continuously compensating for all nutritionallosses. Both compensation elements are automatically managed by at leastsome of the systems, methods, apparatus, and/or code instructionsdescribed herein. This would also free nurses' time.

At least some of the systems, methods, apparatus, and/or codeinstructions described herein address the technical problem by computinga risk of likelihood of a future reflux event by a model (e.g.,classifier component of the model) that is dynamically updated bycorrelations computed for monitored reflux-related parameters and refluxevents that occurred for the patient being enteral fed according to abaseline feeding profile. The risk of future reflux event is computedbased on scheduled and/or predicted reflux-related parameters. Thebaseline feeding profile is dynamically adjusted based on the predictedlikelihood of future reflux events, for reducing risk of the futurereflux event. The model training and adjustment are iterated over time,for real time adaptation of the baseline feeding profile, which resultsin an optimal trade-off between reaching the target nutritional goalwhile reducing risk of reflux. Effectively, factors that cause thepatient reflux are identified in advance and the feeding rate isadjusted appropriated to reduce risk of reflux. When no factors linkedto reflux are identified in the near future, the feeding rate may beincreased (e.g., up to a maximal defined rate). The adjustment isdynamically performed, while monitoring data is continuously gatheredand analyzed to fine tune the baseline feeding profile. For example, aregularly scheduled patient sheet change is detected which is correlatedwith reflux. The rate is reduced in advance, but reflux still occurs.For the next sheet change, the rate is further reduced until no refluxoccurs, or until a small amount of reflux below a threshold is obtained.

An example for adjustment of the feeding rate by the model is nowdescribed. The target enteral feeding rate is 60 [milliliter/hour(ml/hr)] and the pump stroke volume is 2.5 [ml] which leads to a strokefrequency corresponding to a stroke every 2.5 [minutes]. Now, when 4strokes are lost or halted due to reflux or for other reason, the modelgenerates instructions for compensating the loss by increasing after awhile, the pumping rate to for example 1.5 strokes per minute for aperiod of time for making up of the loss. Note that a limit to themaximal rate is defined, for example, about 1 stroke per minute whichcorrespond to about 150 [ml/hr] rate on continuous operation.

It is noted that the systems, methods, apparatus, and/or codeinstructions described herein do not simply perform automation of amanual procedure. First, no manual equivalent of the process describedherein has been previously described. The process of manuallydetermining the enteral feeding profile based on predictive equationsand/or manually measuring GRV are different than the process describedherein. Second, the process described herein includes automated featureswhich cannot be performed manually by a human using pencil and/or paper.For example, computation of the correlations between reflux-relatedparameters and reflux events cannot be manually computed, a model cannotbe manually trained, and a computation of risk of likelihood of futurereflux events cannot be manually computed.

At least some of the systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) described herein relate to the problem of selectinghow and/or when to deliver water, and/or enteral formula and/ormedications to an enteral fed patient for reducing risk of reflux. Atleast some of the systems, methods, apparatus, and/or code instructions(i.e., stored in a memory and executable by one or more hardwareprocessors) described herein improve the medical field of treatment of apatient by improving delivery of water, and/or enteral formula and/ormedications to an enteral fed patient for reducing risk of reflux. Insome implementations, the technical solution to the problem and/or theimprovement are obtained by the model that is trained on the featurevector of a large number of parameters. The model learns correlations ofthe parameters with reflux, and adjusts the enteral delivered substancesaccording to an input of current value of the parameters, for reducingrisk of reflux. The model is iteratively updated with values after theadjustment (which are indicative of results of the adjustment), anditeratively learns to further improve the adjustment to further reducerisk of reflux.

At least some of the systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) described herein relate to the problem of treatinga patient by parenteral nutritional. The standard of care suggests usingenteral nutrition if gastric tolerance allows it. Some studies, forexample, as described with reference to Casaer M P, Mesotten D, HermansG, Wouters P J, Schetz M, Meyfroidt G et al. (2011) Early versus lateparenteral nutrition in critically ill adults. N Engl J Med 365:506-17,have been understood in such a way that parenteral nutrition per se maybe harmful and should not be prescribed in critically ill patients oronly late in the clinical course. This is despite the fact that manyrecent papers, for example, as described with reference to Harvey S E,Parrott F, Harrison D A, Bear D E, Segaran E, Beale R, et al. (2014)CALORIES Trial Investigators. Trial of the route of early nutritionalsupport in critically ill adults. N Engl J Med 371:1673-84, have shownthe safety of parenteral nutrition. This reluctance to prescribeparenteral nutrition in cases of enteral nutrition failure is leading tonegative caloric and nitrogen balance and may lead to impaired clinicaloutcomes, for example, as described with reference to Dvir D, Cohen J,Singer P (2005) Computerized energy balance and complications incritically ill patient.s: an observational study. Clin Nutr 25:37-44.Supplemental parenteral nutrition should be considered in any case ofrisk of undernutrition if enteral feeding does not reach the targetafter 3-7 days, for example, as described with reference to Singer P,Reintam Blaser A, M Berger M M, Alhazzani W, Calder P C, Casaer M (2018)ESPEN guideline on clinical nutrition in the intensive care unit. ClinNutr Epub ahead of publication. At least some implementations of thesystems, methods, apparatus, and/or code instructions described hereincompute a personalized optimal combination of enteral and parenteralnutrition for each patient.

At least some of the systems, methods, apparatus, and/or codeinstructions (i.e., stored in a memory and executable by one or morehardware processors) described herein relate to the medical problem ofimproving breathing of a patient by control of mechanical ventilationand/or fluid balance, and/or improving urination of the patient bycontrolling water (e.g. IV, enteral routes, saline, or other fluids)and/or medications (e.g., diuretic, antidiuretic). Proper breathing andfluid balance are interlinked in compromised patients. Providing toomuch fluid to the patient (e.g. IV, enteral route, and/or usingantidiuretic medications) may properly hydrate the patient but comes ata risk of fluid entering the lungs, which reduces the ability of thepatient to breathe properly even when being mechanically ventilated. Themechanical ventilator may be adjusted to help the patient breath, buttoo much ventilating may damage the lungs of the patient, while whenventilation is insufficient the patient does not get enough oxygen. Toolittle fluid provided to the patient (e.g., too little IV fluid, toolittle enteral fluid, excess use of antidiuretics) may reduce risk offluid in the lungs, but comes at the cost of increased risk in reducedurine output, damage to kidneys, dehydration, and/or buildup of toxinsin the body. Therefore, tradeoffs affecting ventilation and fluidbalance are difficult to manage, especially when performed manually asin common clinical practice. At least some of the systems, methods,apparatus, and/or code instructions (i.e., stored in a memory andexecutable by one or more hardware processors) described herein providea solution to the medical problem by the trained model that monitorsdata elements for adjusting the mechanical ventilator and/or fluidbalance to obtain the target patient-breathing parameter (e.g., SpO2value, value of output of impedance sensor(s)) and/or targetpatient-fluid parameter (e.g., urine output over a time interval).

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, and any suitable combination of theforegoing. A computer readable storage medium, as used herein, is not tobe construed as being transitory signals per se, such as radio waves orother freely propagating electromagnetic waves, electromagnetic wavespropagating through a waveguide or other transmission media (e.g., lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As used herein, the term model may refer to one or multiple models, forexample, artificial intelligence code and/or machine learning codeand/or adaptive system and/or statistical classifiers. The model mayinclude multiple components, for example, a statistical classifierand/or other code. For example, multiple components may be trained,which may process data in parallel and/or as a pipeline. For example,output of one type of model (e.g., from intermediate layers of a neuralnetwork) is fed as input into another type of model. For example, in anexemplary implementation a classifier component of the model is trainedfor predicting likelihood of a future reflux event according to definedinputs. An adjustment to the baseline feeding profile is computed byanother component of the model, for example, for reducing likelihood ofthe future reflux event outputted by the classifier component. Exemplarymodels may include one or more statistical classifiers, which may beimplemented as, for example: one or more neural networks of variousarchitectures (e.g., artificial, deep, convolutional, fully connected),support vector machine (SVM), logistic regression, k-nearest neighbor,decision trees, and combinations of the aforementioned.

Reference is now made to FIG. 1, which is a flowchart of a method fortreating a patient by automated adjustment of a baseline feeding ratefor enteral feeding according to likelihood of future reflux event(s),in accordance with some embodiments of the present invention. Referenceis also made to FIG. 2, which is a block diagram of components of asystem 200 for treating a patient by automated adjustment of a baselinefeeding rate for enteral feeding according to likelihood of futurereflux event(s), in accordance with some embodiments of the presentinvention. One or more acts of the method described with reference toFIG. 1 may be implemented by components of system 200, as describedherein, for example, by a hardware processor(s) 206 of a computingdevice 208 executing code instructions stored in a memory (also referredto as a program store) 210.

Computing device 208 may receive data via one or more data interfaces212. Data may include monitored reflux-related parameters, for example,from one or more of: a camera and/or motion sensors 202A that capturesimages of the patient (and/or by code that analyzes the images and/orcode that analyzes output of the motion sensors to compute an indicationof the reflux-related parameters), and an electronic health record (EHR)of the patient obtained from an EHR dataset 202B (e.g., stored on an EHRserver). EHR dataset 202B may store data indicative of past and/orfuture scheduled administration of diuretics, antidiuretics, and/orfluid (e.g., IV saline). Data may include an indication of refluxevent(s) and/or reflux-event parameters, for example, from one or moreof, impedance sensors 202C (e.g., located on a tube positioned insidethe esophagus of the patient), and reflux evacuation reservoir data 202D(e.g., computed by code that analyzes the amount of reflux evacuatedinto a reservoir). Data may include output by one or more other patientsensors 202E, for example, urinalysis device, pulse oximeter (e.g., forsensing SpO2), urine sensor (e.g., sensing amount of urine and/orconcentration of urine), calorimetry sensor, blood test machine, lungfunction machine, vital sign measurement devices (e.g., blood pressuresensor, heart rate sensor, breathing rate sensor, temperature sensor).Other sensors 202E may be described in the applications incorporated byreference listed herein. Feeding tube sensor(s) 202F which are locatedon the distal end portion of the feeding tube used to deliver theenteral feeding, for example, impedance sensors. Exemplary sensors(s)located on feeding tube 202F are described, for example, with referenceto U.S. patent application Ser. No. 16/000,922, and International PatentApplication No. IB2017/057702 (published as WO2018/104888).

It is noted that motion sensors 202A may be referred to herein usingother implementations, for example, inertial sensors, 6 degree offreedom sensors, gyro sensors, and/or accelerometers.

Additional details of impedance sensors 202C and/or reflux evacuationreservoir data 202D are described, for example, with reference toapplication Ser. No. 15/614,641.

Data interface(s) 212 may be implemented, for example one or more of: anetwork interface, a port, a direct link, a wire connection, a wirelessconnection, a local bus, other physical interface implementations,and/or virtual interfaces (e.g., software interface, applicationprogramming interface (API), software development kit (SDK)).

Computing device 208 may be implemented as, for example, a standaloneunit, a client terminal, a server, a computing cloud, a mobile device, adesktop computer, a thin client, a Smartphone, a Tablet computer, alaptop computer, a wearable computer, glasses computer, and a watchcomputer. Computing device 208 may be implemented as a customized unitthat include locally stored software and/or hardware that perform one ormore of the acts described with reference to FIG. 1. Alternatively, oradditionally, computing device 208 may be implemented as codeinstructions loaded on an existing computing device. Alternatively, oradditionally, computing device 208 may be implemented as hardware and/orcode instructions (e.g., an accelerator card) installed and/orintegrated within an existing computing device, for example, as aplug-in component.

Processor(s) 206 of computing device 208 may be implemented, forexample, as a central processing unit(s) (CPU), a graphics processingunit(s) (GPU), field programmable gate array(s) (FPGA), digital signalprocessor(s) (DSP), and application specific integrated circuit(s)(ASIC). Processor(s) 206 may include one or more processors (homogenousor heterogeneous), which may be arranged for parallel processing, asclusters and/or as one or more multi core processing units.

Memory (also known herein as a data storage device) 210 stores codeinstructions executable by processor(s) 206, for example, a randomaccess memory (RAM), read-only memory (ROM), and/or a storage device,for example, non-volatile memory, magnetic media, semiconductor memorydevices, hard drive, removable storage, and optical media (e.g., DVD,CD-ROM). Memory 210 stores code instruction that implement one or moreacts of the method described with reference to FIG. 1. Alternatively, oradditionally, one or more acts of the method described with reference toFIG. 1 are implemented in hardware.

Computing device 208 may include a data storage device 214 for storingdata, for example, monitoring database 214A that stores monitored data,for example indications of: reflux-related parameters, reflux-eventparameters, and reflux events. Data storage device 214 may beimplemented as, for example, a memory, a local hard-drive, a removablestorage unit, an optical disk, a storage device, and/or as a remoteserver and/or computing cloud (e.g., accessed via a network connection).

Computing device 208 includes and/or is in communication with a userinterface 216 that includes a mechanism for a user to enter data (e.g.,patient information, baseline feeding profile) and/or view presenteddata (e.g., adaptations of the baseline feeding profile). Exemplary userinterfaces 216 include, for example, one or more of, a touchscreen, adisplay, a keyboard, a mouse, and voice activated software usingspeakers and microphone. External devices communicating with computingdevice 208 may serve as user interfaces 216, for example, a smartphonerunning an application may establish communication (e.g., cellular,network, short range wireless) with computing device 208 using acommunication interface (e.g., network interface, cellular interface,short range wireless network interface). The user may enter data and/orview data on the display of the smartphone, optionally via a graphicaluser interface (GUI) application.

Computing device 208 is in communication with an enteral feedingcontroller 204 that controls enteral feeding of the patient via anenteral feeding tube. Enteral feeding controller 204 controls and/oradjusts the rate of the enteral feeding according to instructionsgenerated by computing device 208 based on the baseline feeding profileand adjustments thereof. Enteral feeding controller 204 (and/or anotherdevice) may adjust the composition of the enteral feeding according toinstructions generated by computing device 208. Enteral feedingcontroller 204 may be implemented using a mechanical based mechanism,and/using computer components (e.g., processor(s), memory storing codeinstructions executable by the processor(s), and/or hardwarecomponents). Enteral feeding controller 204 may be implemented as a pump(e.g., positive displacement feed pump) that is controlled to deliverenteral feedings to the patient via the enteral feeding tube accordingto the rate defined by the instructions generated by computing device208. Enteral feeding controller 204 may include a valve that selectivelyopens the lumen of the enteral feeding tube so that enteral feeding maybe delivered to the patient at the defined rate.

As described herein, the term automated enteral feeding device may referto one or more of: system 200 (and components thereof), computing device208, enteral feeding controller 204, and/or other components of system200, and/or combination thereof.

Computing device 208 is in communication with a mechanical ventilator250 that automatically ventilates the patient. Current settings ofmechanical ventilator 250 may be fed into the model, as describedherein. Computing device 208 may output instructions for adjustment ofmechanical ventilator 250, as described herein. Additional details of anexemplary mechanical ventilator are described, for example, withreference to U.S. patent application Ser. No. 16/000,922.

It is noted that alternatively to impedance sensor(s) 202C, othersensors for sensing reflux may be used (termed herein reflux sensors),and/or an indication of reflux computed by another computing device maybe provided. Exemplary reflux sensor include one or more of: a pressuresensor that outputs electrical signals indicative of a sensed pressurewithin the stomach and/or esophagus, an impedance sensor that outputselectrical signals indicative of a sensed impedance within the stomachand/or esophagus, and a pH sensor that outputs electrical signalsindicative of a sensed pH with the stomach and/or esophagus. Refluxevent sensors may be located within the digestive system of the patient,for example, within the duodenum, the stomach, the esophagus, and/orother parts of the digestive system. Reflux event sensors may belocated, for example, on and/or within an enteral feeding tube that isdelivering enteral nutrition to the digestive system of the patient,located on and/or within an evacuation tube that removes refluxinggastric contents (as described herein), located on and/or within anoptional gastric tube that delivers enteral feedings and removesrefluxing gastric contents, and/or located on and/or within a separateprobe.

A reflux evacuation reservoir may receive the reflux that left the bodyof the patient. Reflux evacuation reservoir may be implemented as, forexample, a disposable bag, and/or a disposable container. Refluxevacuation reservoir may be connected to a vacuum source, or no vacuumsource may be used (i.e., based on passive evacuation).

The amount and/or volume of the digestive contents that exited the bodyof the patient may be computed and provided to the computing unit asreflux evacuation reservoir data 202D.

Additional optional components and/or additional optional features ofcomponents are provided, for example, with reference to application Ser.No. 15/614,641 and International Patent Application No. IL2017/051271.

Referring now back to FIG. 1, at 102, a baseline feeding profile isprovided. The baseline feeding profile is defined over a feeding timeinterval, for example, over 4, 6, 8, 12, 24 hours, or other time frames.The baseline feeding profile may define a target nutritional goal toreach at the end of the feeding time interval. The target nutritionalgoal may denote an accumulation of multiple enteral feeding parametersto reach at the end of the feeding time interval, for example, totalvolume of the enteral feeding, total number of calories to provide tothe patient, amount of protein to provide to the patient, and/or othernutrients to provide to the patient.

The nutritional goal may be changed during the feeding time interval(e.g., manually by a physician, automatically by code). The adjustmentto the baseline feeding profile and/or the baseline feeding profile maybe re-computed according to the adjusted nutritional goal. Thenutritional goal may define a maximal limit for the feeding timeinterval, for example, maximum calories and/or maximum volume over 24hours.

The baseline feeding profile may define a feeding rate, which may varyover the feeding time interval, for example, a temporal feeding profile.The baseline feeding profile may define a feeding formulation, which maybe selected, for example, as described with reference to InternationalPatent Application No. IL2017/051271).

The baseline feeding profile may be provided manually selected by a userand/or automatically computed by code. The baseline feeding profile maybe computed, for example, based on a resting energy expenditure (REE)computed based on calorimetry sensors (e.g., as described with referenceto International Patent Application No. IL2017/051271), based onpredictive equations (e.g., Harris-Benedict), by a classifier that isfed parameters associated with the patient feeding and trained on atraining dataset of baseline feeding profiles of feeding parameterssample patients.

The baseline feeding profile may include a maximal feeding rate. Themaximal feeding rate denotes a maximum feeding rate which is not to beexceeded by the feeding controller. The maximal feeding rate is not tobe exceeded when adjustments to feeding rate are made, as describedherein. The maximal feeding rate may be computed according to a risk oflikelihood of future reflux event below a requirement for no scheduledand/or predicted reflux-related parameters. The maximal feeding rate maybe manually selected by a user (e.g., physician). The maximal feedingrate may be set according to a feeding rate tolerated by an enterallyfed patient for which risk of reflux events are low (i.e., no expectedupcoming change of patient position that increase risk of reflux event).

An exemplary process for setting the baseline feeding profile is nowdescribed. It is noted that other methods may be used, and/or theexemplary method may be adapted. The process may be executed by a GUIpresented on a display associated with the computing device. An REEcalculation is performed for determining the energy amount the patientis using, in order to determine the feeding plan for the baselinefeeding profile. The REE measurement duration is defined. Once theduration is set (e.g., while patient is in resting) the measurement islaunched. The measurement may be stopped if the patient is restless orfor any other reason. After the duration has elapsed, the REE iscalculated from the accumulated VCO₂ measurement (e.g., by a CO₂sensor). The results may be accepted, or if they seem erroneous, theymay be rejected. Once the REE measurement is accepted, the foodcalculator may be launched. If the operator selects to exit this panelit returns to the regular REE panel. The food calculator assists inselecting the optimal feeding material based on the REE calculation,hidden calories (optional to insert this information by the user),protein calculation and other optional filters (e.g., low fiber). Basedon the food calculator's parameters and the selected nutrition, thevolume to be delivered (VTBD) and nutrition basal rate are determined,and may be edited by the user. The user is asked by the GUI to set Maxrate (per hour) and Max VTBD (per the duration of the feeding timeinterval, for example 24 hours), this will enable automatic adjustmentsin order to compensate for feeding stop time (e.g., because of refluxevents or routine procedures by the caregivers like bedding, CT scans,surgeries, and the like) and for the residual loss in the GRV residualbag (which is weighted and/or flow is measured by a GRV flow sensor).The GRV mechanism is for decompressing the stomach in reflux eventand/or changes in REE estimations that is within the determined range ofthe user. When VCO₂ exceeds the range set (e.g., threshold changed by+\−15%), an alert is generated. If Max VTBD is defined a new kCAL/daytarget is automatically computed and proposed, along with a newlyderived VTBD and nutrition rate. Once accepted, a new baseline feedingplan is set. Additional exemplary details are described with referenceto International Patent Application No. IL2017/051271.

Optionally, the baseline feeding profile is divided into portions withan expectation that there will be one or more pauses in feeding (e.g.,due to reflux events, scheduled procedures and/or patient orientationevents). For example, the baseline feeding profile to reach the targetnutritional goal is computed for 18 hours rather than 24 hours, based onan expectation that there will be 6 hours of a pause in enteral feeding.In this manner, compensation for the 6 hours of no feeding is computedin advance. Alternatively, or additionally, the pauses in feeding arenot initially considered, and the baseline feeding profile is computedfor the full-time interval (e.g., 24 hours). Pauses and compensationsare dynamically computed as described herein, for example, by limitedtime increase in the pumping rate.

Food type and/or additives may be selected a-priori by the physician andentered into the computing device based on the offering list presentedto him/her according to the hospital available inventory. In anotherimplementation, an updatable database of feeding materials is embeddedin the computing device, or communicated to the computing device via thehospital system to create a personalized top list of the materials thatfit the patient according to the baseline feeding profile.

At 104, the patient is automatically enterally fed by an enteral feedingcontroller (e.g., 204 described with reference to FIG. 2) according tothe baseline feeding profile, which may include the target nutritionalgoal.

The patient is fed according to a baseline feeding rate defined by thebaseline feeding profile.

At 106, multiple reflux-related parameters and/or an occurrence ofreflux event(s) are monitored while the patient is automaticallyenterally fed.

Reflux-related parameters are collected in association with a detectedreflux event. The reflux-related parameters may be collected for a timepoint and/or time interval prior to, and/or during the detected refluxevent. The reflux-related parameters may be tagged with an indication ofthe detected reflux event for training the model (e.g., classifiercomponent of the model) and/or computation of correlations by the model(e.g., classifier component of the model).

Optionally, reflux-related parameters are collected when no reflux eventis detected. Such reflux-related parameters may be tagged with acategory and/or label indicative of no reflux event when training themodel (e.g., classifier component of the model) and/or computingcorrelations with reflux events by the model (e.g., classifier componentof the model).

Reflux-related parameters and/or reflux events may be time stamped.

The reflux events may be monitored and/or detected for example, bysensing output of impedance sensor(s) 202C and/or output of refluxreservoir evacuation dataset 202D. Exemplary systems and methods formonitoring and/or detecting reflux events are described, for example,with reference to application No. application Ser. No. 15/614,641).

The reflux-related parameters denote parameters which are statisticallysignificantly correlated with increased and/or decreased risk of refluxevents. The reflux-related parameters may be monitored by sensor(s)and/or code that monitors values (e.g., of fields) in a dataset.

The reflux-related parameters may be collected continuously, and/orduring preset intervals (e.g., every 5 minutes, 10 minutes, or othertime intervals), and/or triggered by events (e.g., during patientorientation change).

The reflux-related parameters may be collected when a reflux event isdetected, and/or may be collected when no reflux event is detected. Thegastric reflux event denotes digestive contents that have exited thestomach into the esophagus, for example, fluid, stomach acid, andadministered enteral feeding. Gastric reflux events may be minor, inwhich stomach contents travel up the esophagus, but do not leave thebody of the patient. Gastric reflux events may be severe, where stomachcontents exist the body of the patient, for example, into a reservoirdesigned to collect refluxed stomach contents.

Exemplary reflux-related parameters and exemplary processes formonitoring thereof include:

-   -   Time of day. Time of day may be obtained, for example, from an        internal clock, and/or querying a network clock. When a reflux        event is detected, the time of day (e.g., range, start time,        and/or end time) when the reflux event occurred is obtained.    -   Enteral feeding rate. The enteral feeding rate may be obtained,        for example, from code that computed the current enteral feeding        rate, from a field in memory that stores the current enteral        feeding rate, and/or by querying the enteral feeding controller.        When a reflux event is detected, the obtained enteral feeding        rate may be for the feeding rate prior to the reflux event        (e.g., immediately before), since the enteral feeding may be        paused during the reflux event itself.    -   Patient location (also referred to as orientation and/or        position), for example, lying down, rotated to left, rotated to        right, reclining (e.g., angle of recline). Changes in patient        location may be monitored, for example, a change in angle of        recline, and/or a change from left rotation to right rotation.        When a reflux event is detected, the patient location (and/or        change thereof) may be for the patient location (and/or change        thereof) prior to the reflux event (e.g., immediately before, a        few seconds or minutes before, according to a threshold).

It is noted that other reflux-related parameters may be discovered andmonitored. A combination of reflux-related parameters may be monitored.Such reflux-related parameters have a statistically significantcorrelation with risk of reflux even. Different patients may havedifferent correlation values. Some reflux-related parameters may bestatistically significant for some patients and not significant forother patients. For example, some patients may have a reflux event witha certain change in orientation while other patients do not suffer areflux event for the same (or similar) change in orientation. Exemplaryother reflux-related parameters include: one or more blood test values,one or more urinalysis test values, one or more lung function parametervalues, and one or more values of vital signs.

Patient location changes may occur, for example, during changing of thepatient's bedding (e.g., sheets, blankets, pillows, and/or coveringsthereof).

Patient location changes may occur during patient procedures, forexample, performing tracheal suction, and/or cleaning the patient (e.g.,bathing the patient).

The patient location change may detect based on, for example, scheduledevent requiring patient location change extracted from an electronichealth record (EHR) of the patient, and/or an analysis of imagescaptured by a camera monitoring the patient (e.g., a neural network thatreceives the images and outputs a classification category indicative ofpatient location).

6 degree of freedom (DF) inertial sensors output signals (e.g.,acceleration and/or angular rate, for example, gyros) may be attached tothe patient (e.g., body, clothing, straps, clips), which may provide analternative and/or additional option for correlating patient motion withreflux occurrence.

-   -   Administered medication. When a reflux event is detected, the        medications administered prior to the reflux event are obtained.        The administered medications may be obtained, for example, from        the EHR of the patient.

The reflux-related parameters may be stored in the monitoring dataset.

At 108, optionally, when a reflux event is detected by the monitoringprocess while the patient is being enterally fed, the enteral feedingmay be paused by the enteral feeding controller. Alternatively, oradditionally, the feeding may be automatically paused when a change inpatient orientation is detected (e.g., when patient procedures are beingperformed) for example, detected by the analysis of images of thecamera. Such change in orientation may occur spontaneously, not beingscheduled in advance in the EHR and/or not following a routine (e.g., atthe same time every day but not documented in the EHR).

A pause in enteral feeding may occur for other reasons, for example, aphysician decision, due to a clog in the feeding tube, a diagnosis ofgastroparesis (e.g., stored in the EHR), and/or a prokinetic medication(e.g., indicative of a problem in gastric intake). It is noted that inact 116, the adjustment to the baseline feeding profile automaticallycompensates for the pause in enteral feeding, by increasing the feedingrate when possible (e.g., risk of reflux is relatively low).

The pause may be for a pause time interval, which may be predefinedand/or according to a detected end of the reflux event itself and/oraccording to the end of the patient orientation changes (e.g., end ofpatient procedure, end of change of patient bedding). Additionalexemplary details of detecting a reflux event, pausing enteral feeding,evacuation of gastric contents during the reflux event (e.g., byestablishment of a passive evacuation channel into a reservoir),detection of termination of the reflux event, and/or resumption offeeding are described, for example, in application Ser. No. 15/614,641.The patient change in orientation may be detected based on analysis ofimages of the patient captured by a camera, and/or based on a sensormonitor orientation of a patient that may be worn on the patient (e.g.,accelerometer, compass, angle sensor).

Optionally, reflux-event parameter(s) are collected during the detectedreflux event and/or the detected change in patient orientation event.Exemplary reflux-event parameter(s) include: reflux duration, and refluxamount. Reflux duration may be computed according to a start time whenthe reflux event was first detected (e.g., based on output of theimpedance sensor(s)) and an end time when the reflux event is detectedas having terminated (e.g., based on output of the impedance sensor(s)).Reflux amount may be computed according to the reflux reservoirevacuation dataset, for example, a sensor that monitors volume of refluxevacuated into a reservoir. It is noted that pausing feeding during thedetected change in patient orientation may prevent or reduce reflux.

The reflux-event parameters may be stored in the monitoring dataset.

Optionally, when the reflux event is detected, an adjustment to thebaseline feeding profile is computed by reducing the current feedingrate. The enteral feeding may be resumed after the pause time intervalhas elapsed, at the reduced feeding rate. Alternatively, when thepatient orientation changes are detected, the adjustment to the baselinefeeding profile may be to increase the baseline feeding rate tocompensate for the loss of feeding occurring during the pause due to thepatient orientation event. The increased in feeding rate may be boundedby the defined maximal feeding rate. The feeding rate may be increased,for example, when the computation of risk of reflux of a pause due topatient orientation change is low, even when the risk of reflux due tothe patient orientation change is high. The patient orientation changemay affect risk of reflux during the change itself, while risk of refluxbefore and/or after the change may be low. Therefore, since the pausedue to patient orientation itself is not associated with risk of reflux,the feeding rate may be increased before and/or after the change whenrisk of reflux remains low. However, it is noted that during a nextiteration, when the patient orientation change (which is associated withincreased risk of reflux) is predicted (e.g., due to a regular recurringpattern, and/or detected from the EHR) the pause may be scheduled inadvance, and the increased feeding rate may be computed in advance forcompensating for lack of feeding during the pause. The compensation mayoccur before and/or after the planned pause.

It is noted that the resumption of feeding at the reduced feeding rateafter the reflux event may be contrary to standard practice and/orintuition, where feeding rate is increased in order to compensate forlosses of reflux. In contrast, as described herein, the feeding rate isgradually increased when risk of reflux is determined to be low, toprevent further reflux events. Compensation for the loss of feedingsduring reflux is performed when risk of reflux is computed to be low.

At 110, a model (e.g., classifier component of the model) is trained forpredicting likelihood of a future reflux event according to an input ofscheduled and/or predicted reflux-related parameters. It is noted thatthe model (e.g., classifier component of the model) is trained for eachpatient being enterally fed. The customized model (e.g., classifiercomponent of the model) improves accuracy of computing an adjustment tothe baseline feeding profile which is best for the current patient, forreaching the target nutritional goal while minimizing reflux eventsand/or severity of reflux events. Conceptually, the model (e.g.,classifier component of the model) learns the feeding profile that isbest tolerated by the current patient.

The outputted likelihood of future reflux event may be, for example, abinary value such as predicted reflux event, or no predicted refluxevent. Alternatively, or additionally, the outputted likelihood offuture reflux event is a category indicative of relative classes ofrisk, for example, high risk, medium risk, and low risk. Alternatively,or additionally, the outputted likelihood of future reflux event is avalue, optionally a continuous value, indicative of probability ofreflux event, for example, about 50%, or about 20%, or about 90%. Theprobability values may be thresholder to create the binary and/orcategory outputs.

The model (e.g., classifier component of the model) may be trainedaccording to computed correlations between the reflux-related parametersand detected reflux event(s) and/or indications of no reflux event(s).The correlations may be a multi-dimensional space, where eachreflux-related parameter denotes a respective dimension. Otherimplementations may be possible, for example, a neural network.

The model (e.g., classifier component of the model) is updated (e.g.,continuously and/or at intervals and/or during defined events) with newmonitored data and/or new detected reflux events and/or no detectedreflux events. The model (e.g., classifier component of the model) maybe updated according to correlations between the reflux-relatedparameters associated with the detected reflux event(s), and thedetected reflux event itself.

The updating of the model (e.g., classifier component of the model) mayupdate the computed correlations. Conceptually, the model (e.g.,classifier component of the model) learns from its past mistakes byimproving the correlations based on the new monitoring data, to providemore accurate predictions for the current patient. The updating may beiteratively performed, as described herein. The updating improvesaccuracy of the computed correlations for the current patient beingenteral fed. The increased accuracy of the correlations enables finetuning the baseline feeding profile according to the ability of thecurrent patient to tolerate enteral feedings to reach the targetnutritional goal, while reducing the reflux events and/or reducingseverity of the reflux events.

Optionally, the reflux event(s) are associated with one or morereflux-event parameters, for example, reflux duration and/or refluxamount (e.g., volume). The model (e.g., classifier component of themodel) may be trained for prediction of likelihood of the future refluxevent based on computed correlations between the reflux-relatedparameters and the reflux-event parameters.

Optionally, the reflux event is defined as a requirement of thereflux-event parameters. The requirement enables defining what qualifiesas a reflux event, such as severity of a reflux event of interest thatshould be avoided. For example, a minimal time that qualifies as areflux event and/or minimal volume that qualifies as a reflux event. Forexample, short and/or small reflux events may be ignored, to focus onreducing risk of significant longer and/or larger reflux events.Alternatively, any time and/or any amount of reflux qualifies as refluxevent, for example, to reduce risk of any reflux event of any severity.

Optionally, the correlations are computed between reflux-relatedparameters and reflux event(s) (and/or no reflux events) (which may beassociated with an indication of time, for example, tags of time)falling within a common time window, for example about 5 minutes, or 10minutes, or 15 minutes, or 20 minutes, or 30 minutes, or 45 minutes, or60 minutes, other intervals. The common time window may enabledetermining cause and effect correlations, where a certainreflux-related parameter that occurred a certain amount of time beforethe reflux event is correlated with the reflux event. For example,administration of medication 10 minutes before the reflux event iscorrelated with risk of brining on the reflux event. Optionally, thecommon timing window is a sliding window that is slide over time stampedmonitoring data. The correlations are iteratively computed according tothe sliding window.

Optionally, the reflux-related parameters denote a time and/or locationwithin a repeating physiological cycle. It is noted that thereflux-related parameter may be the actual time. The patient mayexperience a repeating physiological cycle, where for some parts of thecycle (e.g., day) the patient is able to tolerate faster feeding rates,and for other parts of the cycle (e.g., night) the patient may tolerateslower feeding rates. The correlation may be computed between refluxevent(s) and/or no reflux event(s) and the time and/or location withinthe repeating physiological cycle. The risk of likelihood of futurereflux event is based on a current time (i.e., fed into the trainedmodel (e.g., classifier component of the model)) with respect to therepeating physiological cycle.

At 112, scheduled and/or predicted reflux-related parameters are fedinto the trained model (e.g., classifier component of the model), foroutputting risk of likelihood of a future reflux event.

The scheduled and/or predicted reflux-related parameters may be fed, forexample, triggered in response to new detected data (e.g., new valuesappearing in the EHR of the patient), continuously, and/or at regularlyscheduled time intervals.

The risk of likelihood of the future reflux event may be for a futuretime interval that includes the scheduled and/or predictedreflux-related parameters.

The scheduled and/or predicted reflux-related parameters may include,for example, one or more of:

-   -   A current time. For example, for predictions based on        physiological cycles. It is noted that the current time for        prediction based on the physiological cycle includes future        times, where the future times do not necessarily need to be fed        into the model (e.g., classifier component of the model). When        the physiological cycle is associated with low risk of reflux by        the model (e.g., classifier component of the model), the feeding        rate may be increased. When the physiological cycle is        associated with higher risk of reflux by the model (e.g.,        classifier component of the model), the feeding rate may be        decreased.    -   Enteral feeding rate. For example, according to the feeding        profile.    -   Patient orientation changes. Patient orientation changes may be        scheduled, for example, appearing in the EHR of the patient, for        example, a scheduled patient event where the patient requires        change of location. The patient locations may be predicted based        on a historical analysis of previous patient changes. For        example, the images obtained from the camera capturing images of        the patient indicate change of sheets once a day at        approximately the same time every day and/or signal obtained,        accumulated and analyzed by the patient body strapped inertial        sensors. The future time for the change of sheets may be        predicted.    -   Medication administration. Medications scheduled for        administration to the patient may be obtained, from example,        from the EHR of the patient.

At 114, an adjustment to the baseline feeding profile is computed forreducing likelihood of the future reflux event and for meeting thetarget nutritional goal. The adjustment to the baseline feeding profilemay be computed by the model, for example, by another component of themodel.

The adjustment to the baseline feeding profile may include an adjustmentto a baseline feeding rate delivered by a pump. The stroke rate of thepump, and/or the stroke amplitude of the pump may be adjusted.

Optionally, when the risk of likelihood of the future reflux eventdenotes a likely occurrence of the future reflux event (e.g., accordingto a threshold, range, and/or other requirement), the adjustment is areduction in feeding rate.

Alternatively, or additionally, when the risk of likelihood of thefuture reflux event denotes an unlikely occurrence of the future refluxevent (e.g., according to a threshold, range, and/or other requirement),the adjustment is an increase in feeding rate.

Optionally, the adjustment is for compensating for reduced feedingsprovided to the patient during a predicted and/or scheduled pause infeeding due to change in patient orientation (e.g., scheduledprocedure).

Alternatively, or additionally, the adjustment may be a pause in feedingfor a computed time interval to reduce risk of reflux. For example, areduction in feeding rate may refer to pausing the feeding (i.e., zerofeeding rate) by an amount of time which is adjusted accordingly.Optionally, after the pause in feeding, the adjusted may be to increaseor decreased the feeding rate in comparison to the feeding rate beforethe pause in feeding. For example, when the pause is due to a predictedand/or scheduled change in patient orientation (e.g., scheduledprocedure), which is associated with relatively low risk of reflux, thefeeding rate may be increased to compensate for the loss of feeding dueto the pause. Alternatively, when the pause is due to a predicted and/orscheduled medication, which is associated with relatively higher risk ofreflux, the feeding rate may be reduced to provide the patient withfeeding while reducing risk of reflux due to the medication.

Optionally, the adjustment to the feeding rate is performed for reachingthe target nutritional goal at the end of the feeding time interval. Forexample, for the computed risk of reflux, the feeding rate may beadjusted to 50 milliliters per hour (ml/hour). When 200 milliliters arerequired to reach the nutritional goal, and there are 5 hours left, thefeeding rate may be set to 40 ml/hour rather than 50 ml/hour, since the40 ml/hour is expected to provide the target nutritional goal with aneven lower risk of reflux over the 50 ml/hour. As described herein,feeding rate may be controlled by stroke rate and/or by strokeamplitude.

Optionally, the adjustment to the feeding rate is performed forcompensating for feeding losses occurring due to reflux and/or gastricresidual volume (GRV) procedures and/or pauses in feeding due to otherreasons (e.g., procedures performed on the patient). The compensationmay be performed by increasing the feeding rate above the baselinefeeding profile, below the optional maximal feeding rate, according tothe tolerated risk of reflux event.

The reduction and/or increase in feeding rate may be performed using oneor more processes:

-   -   Proportion according to the risk of likelihood of the future        reflux event. The higher the predicted risk of reflux, the lower        the rate. The lower the predicted risk of reflux, the higher the        rate. The rate may be computed according to risk, for example,        by an inverse function, by a linear correlation, by a        logarithmic correlation, by an exponential correlation, by a        function fitted to a set of points, and/or other methods.    -   By a constant predefined amount. The predefined amount may be        the same or different for the increase and decrease in feeding        rate. When risk is low, at each iteration, the feeding profile        is increased by a defined amount (e.g., lcc/hour or other        value). When risk is high, at each iteration, the feeding        profile is reduced by the defied amount.    -   According to a set of rules based on the computed the risk of        likelihood of the future reflux event. For example, when risk is        high, lower the feeding rate drastically to a low level,        maintain feeding rate at a low level for a time interval to make        sure a low risk has been reached, then slowly titrate the        feeding rate higher while risk remains low within an interval.

The increase in feeding rate may be limited by the maximal feeding rate.The maximal feeding rate may be computed according to a risk oflikelihood of future reflux event below a requirement for no scheduledand/or predicted reflux-related parameters. The maximal feeding rate maydenote the fastest feeding rate tolerated by the patient under bestconditions, where risk of reflux is insignificant, low, and/or accordingto other thresholds.

The adjustment to the feeding profile may be computed based on ananalysis of the amount of reflux that exited the body, for example,collected in the container, sensed by a sensor, and provided by thereflux reservoir evacuation dataset. The feeding profile may be adjustedto compensate for the amount of enteral feeding lost by the refluxevent. Additional details are provided, for example, with reference toapplication Ser. No. 15/614,641.

The adjusted feeding profile may be presented on a display, for example,within a GUI, for example as described below with reference to FIGS.3A-3B. The adjusted feeding profile may be further manually adjusted,for example, via the GUI.

At 116, the patient is treated by enteral feeding delivered by theenteral feeding controller according to the adjusted feeding profile.The feeding rate is according to the adjustment feeding rate.

Instructions for adjustment of the enteral feeding rate by the enteralfeeding controller may be generated according to the computed adjustmentfeeding profile, for example, code, and/or a script, such as combinedcode, source code, human readable code in text format, and/or machinecode. The enteral feeding controller implements the generatedinstructions for automated enteral feeding of the patient according tothe adjusted enteral feeding rate and/or adjusted baseline feedingprofile.

At 118, one or more features described with reference to acts 106-116are iterated. The iterations dynamically update the correlations used bythe model (e.g., classifier component of the model) to computelikelihood of reflux event, dynamically re-compute risk of reflux eventbased on new reflux-related parameters, and dynamically adjust thebaseline feeding profile, optionally by dynamic adjustment of theenteral feeding rate. Effectively, the model (e.g., classifier componentof the model) learns to more accurately predict risk of reflux eventsfrom the monitored data, and fine tunes the feeding rate to attempt toreach the target nutritional goal while reducing reflux events and/orseverity thereof.

Iterations may be performed, for example, after each detected refluxevent, after change in computed likelihood of reflux event, after newreflux-related parameters are obtained (e.g., from the EHR), and/or overthe timer interval at predefined intervals (e.g., once an hour over the24 hours' time interval).

Optionally, the monitoring is performed over a time interval for which arisk of likelihood of the future reflux event was previously predicted.The monitoring may be for data indicating reflux-related parametersand/or reflux events, which has accumulated from the later monitoringiteration. For example, a medication which was previously associatedwith triggering a reflux event is scheduled for administration in thefuture. The mediation is given once a day, and has been given for thepast several day. A few days ago, the medication triggered a refluxevent. The adjustment to the baseline feeding profile was previouslycomputed, for example, in response to the previously detectedadministration of the medication and triggered reflux event, the feedingwas paused yesterday for 10 minutes after the medication wasadministered. A reflux event still occurred, but was less in severity.The training is performed for the time interval and associated refluxevent, for updating the trained model (e.g., classifier component of themodel) based on the monitoring data accumulated since the previoustraining iteration. For example, the correlations are updated based onmonitoring data collected during the pause and the triggered refluxevent. The feeding into the model (e.g., classifier component of themodel) is performed based on the updated trained model (e.g., classifiercomponent of the model) for outputting a new and/or updated risk oflikelihood of the future reflux event. The feeding into the model (e.g.,classifier component of the model) may be iterated for previouslyprocessed and/or new scheduled and/or predicted reflux-relatedparameters. The feeding into the model (e.g., classifier component ofthe model), which is iteratively performed, may re-outputting updatedrisk of likelihood of the future reflux event.

The previously processed reflux-related parameters may be associatedwith updated risk of reflux event due to the updated correlations. Forexample, for the scenario that occurred yesterday of the administrationof the medication and 10 minute pause post administration, likelihood ofthe smaller reflux event is computed. The adjustment is dynamicallycomputed according to dynamically predicted likelihood of future refluxevents. The adjustment may be re-computed according to changes in thelikelihood of future reflux events arising from updated correlationsand/or new reflux-related parameters. The feeding rate may be furtherreduced according to the re-outputted risk, for the same scenario forwhich the feeding rate was previously reduced but still resulted inreflux. The new feeding rate may be further reduced in an attempt toprevent reflux for the repeat scenario. The adjustment is performed tofurther increase the amount of time that the enteral feeding is pausedto further reduce risk of reflux, for example, pause for 20 minutes. The20 minute pause is implemented, which may result in preventing thereflux event. The model (e.g., classifier component of the model) isupdated with the monitoring data that indicates that the 20 minute pauseprevents reflux when the medication is administered.

At 120, optionally, instructions for parenteral feeding of the patientare generated (e.g., code, script, for manual setting of an automatedmachine). The parenteral feeding may be automatically performed by aparenteral feeding controller that implements the generatedinstructions.

The parenteral feeding may be computed at the end of the time interval,as a nutritional difference between the accumulation of enteral feedingparameters delivered according to the dynamically adjusted feedingprofile, and the initially set target nutritional goal. The nutritionaldifference denotes the amount of missing nutrition which was unable tobe enterally provided to the patient due to risk of reflux events. Sincethe missing nutrition cannot be made up enterally (i.e., will triggerreflux events), the missing nutrition may be provided parenteral.

Reference is now made to FIG. 2A, which is a schematic depictingreflux-related parameters 802 and reflux events 804 occurring over atime interval, in accordance with some embodiments of the presentinvention. Correlations are computed between reflux-related parameters802 and reflux events 804, as described herein. Reflux events 804 may bedenoted by appearance of reflux, which may be in a pattern (e.g.,intensity, volume, over a time interval) posing a significant risk fortriggering inspiration pneumonia. For example, high correlation valuesbetween reflux-related parameter 802 and reflux events 804 over athreshold (e.g., about 0.7, or 0.8, or 0.9, or 0.95, or otherintermediate, smaller, or larger values) may generate instructions foradaption of patient management, for example, pausing the enteral feedingor reducing the rate of enteral feeding.

Reflux-related parameters 802 may include: time of day (denoted x1(t)),patient motion (denoted x2(t)), one medication (denoted x3(t)), andanother medication (denoted x4(t)), as described herein. Additional,fewer, and/or alternative reflux-related parameters 802 may be used.Reflux events are denoted y.

Correlations are computed between the reflux-related parameters 802 andreflux events 804, for example, reflux events (y) are correlated todaily events (x1,x2, x3, . . . ) such as patient motion, medicationadministration, and parts of the day (e.g., night, afternoon) which maybe based on a physiological cycle of the patient. The correlations maybe mathematically denoted as [y,x1], [y,x2], [y,x3], [y,x4], andadditional correlations may be used for additional reflux-relatedparameters. The model learns the correlations, for reducing (oravoiding) likelihood of future reflux events, for example, by haltingfeeing before a predicted or scheduled reflux-related event.

Reference is now made to FIG. 2B, which is a schematic depicting anexemplary dataflow of medical signals for automated adjustment ofenteral feeding and/or other control parameters, in accordance with someembodiments of the present invention. The dataflow of FIG. 2B may beimplemented as, and/or integrated with, and/or replaced with, featuresand/or components described with reference to FIGS. 1-8. Data signals,including patient bed side data 810 and/or EHR data 812 (e.g.,patient-related parameters, and/or reflux-event parameters and/orenteral delivered substances, for example, output of sensors and/orscheduled medication administration and/or scheduled events thatindicate change in patient orientation) are fed into AI machine 814(e.g., computing device executing at least code of the trained modeldescribed herein). The data signals are fed into a correlation engine816, which computes multiple correlations, optionally values of weightsdenoted W_(i,j) 818, as described herein. Correlations W_(i,j) outputtedby correlation engine 816, and the input data 810 812 are fed into oneor more sub-component code 820A-E that compute instructions based on thecorrelations, for example, according to a set of rules, a sub-classifiercomponent, and/or according to physician instructions 822. Theinstructions are provided for control of an enteral pump controller 824and/or other actuators, for example, a valve 826 and/orinflator/deflator 828 of a balloon on the end portion of the feedingtube. Optionally, when risk of reflux is high, the balloon is inflatedto prevent reflux from entering the esophagus, and optionally direct thereflux into an evacuation reservoir, for example, as described withreference to patent application Ser. No. 15/614,641. When risk of refluxis low, the balloon is deflated, for example, to prevent or reduce riskof damage to the inner lining of the esophagus from pressure.

Reference is now made to FIG. 2C which is a schematic of anenvironmental perspective of a patient 850 being fed via a feeding tube852 by automatic adjustment of enteral feeding according to computedcorrelations for reduction of reflux computed by a main console 854(e.g., including the computing device described herein that executescode of the model, e.g., AI machine), in accordance with someembodiments of the present invention. Patient-related parameters and/orreflux-related parameters of patient 850 are obtained from output ofsensors 856 (e.g., skin sensors, impedance electrodes, inertial sensors,SpO2 sensor, cardiac sensor, vital sign sensors, skin electrodes, lungfluid measurement electrodes, limb electrodes, and others) and/orpatient monitoring camera 858 and/or urine analyzer 860, as describedherein. Data outputted from sensors may be fed into a hub 862, whichcommunicates with main console 854 and/or may include an enteral feedingcontroller and/or pump. Hub 826 and/or main console 854 may communicatewith the server storing the EHR of the patient. Console 854 receives thedata, computes correlations (e.g., to detect cross effects betweenfeeding parameters, water, and/or medication administration), andgenerates instructions for adjusting enteral substances fed into thepatient (e.g. formula, water, medications), as described herein.

Reference is now made to FIG. 3A, which is a graph depicting an exampleof adjusting a baseline feeding profile 302, in accordance with someembodiments of the present invention. Baseline feeding profile 302(depicted as dotted lines) is provided for 24 hours feeding timeinterval, as described herein. Baseline feeding profile 302 defines afeeding rate (e.g., in milliliters per hours (ml/hr), depicted on they-axis as a function of time depicted on the x-axis. Baseline feedingprofile 302 may define a maximal feeding rate 304 (e.g., 150 ml/hr), asdescribed herein. At the start of the feeding session, the patient isfed according to the rate 306 defined by baseline feeding profile 302(e.g., about 40-45 ml/hour). A first feeding pause A 308 occurs. Feedingpause 308 may occur, for example, due to a scheduled procedure on thepatient that requires pausing feeding. When pause 308 ends, the feedingrate may be increased 310 to a rate above the initial baseline feedingprofile 302, in order to compensate for the pause in feeding. The ratemay be increased when the model (e.g., classifier component of themodel) computes a relatively low risk of reflux events. At 312, a secondfeeding pause B occurs due to a detected reflux event. Feeding resumes314 when the reflux event has ended, at a rate that is below the initialbaseline feeding profile 302 and below the previous feeding rate 310, inorder to reduce risk of another reflux event. Graph 316 depicts theaccumulated amount of food provided to the patient during the feedinginterval at time 10:00 AM (depicted by arrow 318 relative to thebaseline feeding profile 302) relative to a target nutritional goal 320(e.g., 1500 ml).

Reference is now made to FIG. 3B, which is a graph depicting the processof adjusting baseline feeding profile 302 to reach target nutritionalgoal 320, in accordance with some embodiments of the present invention.The graph of FIG. 3B includes graph FIG. 3A, which describes adjustmentof baseline feeding profile 302 until 10:00 AM, and includes details ofadjustment of baseline feeding profile 302 for the full 24 hours of thefeeding time interval. Feeding continues until another scheduled feedingpause C 322 is reached. When pause 322 ends, the feeding rate may beincreased 324 to a rate above the initial baseline feeding profile 302,in order to compensate for the pause in feeding. The rate may beincreased when the model (e.g., classifier component of the model)computes a relatively low risk of reflux events. At 326, a gastroresidual volume (GRV) event occurs, where a significant amount ofdigestive contents are evacuated from the digestive system (e.g.,stomach) of the patient, as denoted by the negative feed rate values.The GRV event may occur, for example, due to reflux of the patient,and/or a scheduled GRV procedure. At 328, the feeding rate may beincreased to a rate above the initial baseline feeding profile 302, inorder to compensate for the GRV losses. The rate may be increased whenthe model (e.g., classifier component of the model) computes arelatively low risk of reflux events. It is noted that at all times theadjusted feeding rate is maintained below maximal feeding rate 304. Atend of the 24 hours feeding interval, graph 316B indicates that thetarget nutritional goal 320 has been met.

Reference is now made to FIG. 4, which includes equations for computingan estimation of an amount of enteral feeding lost due to reflux and/ora GRV procedure, for compensating by adjustment of the baseline feedingrate, in accordance with some embodiments of the present invention. Theamount of lost water can be calculated by subtracting the lost foodvolume V_(f) from the total volume V_(t).

An exemplary implementation of a model (e.g., classifier component ofthe model) based on the monitoring data is now described. The model(e.g., classifier component of the model) is based on the χ² technique,curve fitting, and/or adaptive optimum processes.

Reference is now made to FIG. 5, which is a graph that presents a refluxevent denoted y[m] that is associated with reflux-related parametersdenoted x[n] in a range denoted a[n,m] and b[n,m], where it is assumedthat N reflux events are present and M possible reflux-relatedparameters are considered, in accordance with some embodiments of thepresent invention.

When patient reflux-related parameters denoted X are within the rangedenoted a[n,m] and b[n,m] it is concluded that the patient hasexperienced reflux event denoted y[m] or, logically stated:

∩_(n=0) ^(n=N-1) {a[n,m]≤X≤b[n,m]=1} then Y=y[m]

Reference is now made to FIG. 6, which depicts a normal distribution ofthe accumulated reflux-related parameters associated with reflux events,in accordance with some embodiments of the present invention.

When data for a specific patient is analyzed the resulting measuredparameters will be used to test for a series of null hypothesis eachassociated with one of the M potential reflux events:

H ₀ ^(m)(0 . . . M−1)

The associated Chi square value associated with each of the M hypothesis(one of the M potential reflux events) will be given by:

${\chi^{2}\lbrack m\rbrack} = {\sum\limits_{n = 0}^{n = {N - 1}}\; \frac{\left\lbrack {{{x\lbrack n\rbrack}{measured}} - {{\overset{\_}{x}\lbrack n\rbrack}{accumulated}}} \right\rbrack^{2}}{{\overset{\_}{x}\lbrack n\rbrack}{accumulated}}}$

Reference is now made to FIG. 7, which is a graph depicting resultingvalue of each of the M calculated χ²[m] compared with the ref value χ²^(ref) taken from a standard χ² table under the desired confidence levelP and the number of degrees of freedom N−1, in accordance with someembodiments of the present invention.

The decision is taken to be reflux event m* of the M possible for which:

χ²[m*] is the smallest of all χ²[m]′s

And

χ²[m*]<χ² ^(ref)

If non-fits additional tests are indicated.The a's and b's are obtained from the monitored dataset and asadditional data is gathered, they are updated by:

a[n,m]=a[n,m]+Δa[n m]

b[n,m]=b[n,m]+Δb[n,m]

The Δ's are corrections of the limits based on new accumulated data. Forthe case where, normal distribution is accumulated and used, thedistribution will also be updated when additional statistics isaccumulated.

Reference is now made to FIG. 8, which is a flowchart of an exemplarymethod for generating instructions for treating a patient by automatedenteral feeding controlled by a trained model, in accordance with someembodiments of the present invention. The method described withreference to FIG. 8 may include, and/or substitute for, and/or becombined with, features and/or components described with reference toFIGS. 1-7.

It is noted that the model described herein may refer to the model ofFIG. 1, and/or the model of FIG. 8, and/or combinations thereof. Forexample, data elements used to train the model of FIG. 8 may be used totrain the model of FIG. 1.

At 902, data is obtained. The data may be obtained by a monitoringprocess over a monitoring interval. The data may be obtained while thepatient is automatically enteral fed by an enteral feeding controller.The data may be obtained from one or more components 202A-E.

The patient may be enteral fed according to a baseline feeding profile,which may include a target nutritional goal, as described herein.

One or more of the following exemplary data may obtained:

-   -   Patient-related parameters. The patient-related parameters may        include static and/or dynamic values associated with the        patient.

Exemplary patient-related parameters include: patient demographics,patient age, patient gender, current patient medical diagnosis, pastpatient medical history, current patient signs and/or symptoms, patientvital signs, patient urine data, patient calorimetry data, enteralfeeding rate, patient location changes, blood test values, urinalysistest values, lung function parameter values.

-   -   Enteral delivered substances. The enteral delivered substances        include one or more substances that are enteral delivered to the        patient. Exemplary enteral delivered substances include: enteral        feeding (e.g., formula), water, and one or more medications.

The enteral delivered substances may be defined by the baseline feedingprofile, and/or for reaching the target nutritional goal.

-   -   Reflux-event parameters. The reflux-event parameters define one        or more aspects of the reflux event. Exemplary reflux-event        parameters include: time of day of the reflux event, volume of        reflux, intensity of reflux, duration of reflux, weight of        reflux.

At 904, a training dataset is created by computing one or more featurevectors. Each feature vector stores (e.g., an indication of) theobtained data elements, including the patient-related parameters, and/orthe enteral delivered substances, and/or the reflux-event parameters,for example, as a large vector where each value of each parameter isstored in an element of the feature vector.

Each feature vector may be associated with an indication of time duringthe monitoring interval indicating when the respective data element wasobtained. Alternatively, or additionally, individual data elements areeach associated with an indication of time when the respective dataelement was obtained. The time may be stored as an element of thefeature vector.

Each feature vector may store data elements obtained during a commontime interval, for example, within a 10 minute interval, or othervalues.

At 906, a model is trained and/or created based on the training dataset.The model is adapted to receive current patient-related parameters andoutput instructions for adjustment of the enteral delivered substances.The adjustment of the enteral delivered substances may be for reducinglikelihood of a future reflux event.

The model may be trained according to computed correlations between thepatient-related parameters, and/or the enteral delivered substances,and/or the reflux-event parameters. The correlations may be indicativeof which patient-related parameters and/or enteral delivered substances,alone or in a combination, are associated with various risks of refluxevents having varying values of reflux-event parameters.

At 908, current patient-related parameters and/or current enteraldelivered substances are fed into the trained model. The model outputsinstructions for adjustment of the enteral delivered substances. Theadjustment may be selected for reducing likelihood of a future refluxevent.

For example, the adjustment may be for entering a medication phase whenadministration of medication is indicated, by temporarily halting (i.e.,pausing) feeding for a predefined time interval for reducing likelihoodof reflux.

In another example, the adjustment defines how much water to enteralprovide to the patient, and when in time to provide the water. The waterproviding may be selected to reduce likelihood of reflux.

In another example, the adjustment defines when in time to administer ascheduled medication to the patient. The medication administration maybe selected to reduce likelihood of reflux.

At 910, the enteral delivered substances are adjusted according to theinstructions.

At 912, one or more of features 902-910 are iterated over time. Thereceiving of data of 902, the creating the dataset of 904, and thetraining the model of 906 are iterated over time to update the modelwith responses to the adjustment of the enteral delivered substances.The model learns the effects of its adjustment decisions, anditeratively improves the decision making ability to reduce risk ofreflux and/or improve delivery of enteral delivered substances. Featuresof 908-910 are iterated over time for new data values using the updatedmodel.

Reference is now made to FIG. 9, which is a flowchart of an exemplarymethod for generating instructions for treating a patient by adjustmentof a ventilator and/or fluid balance of a patient according toinstructions based on output of a trained model, in accordance with someembodiments of the present invention. The method described withreference to FIG. 9 may include, and/or substitute for, and/or becombined with, features and/or components described with reference toFIGS. 1-8.

It is noted that the model described herein may refer to the model ofFIG. 1, and/or the model of FIG. 9 and/or other implementations of themodels described herein, and/or combinations thereof. For example, dataelements used to train the model of FIG. 9 may be used to train themodel of FIG. 1.

It is noted that the implementations of the models described herein maybe combined into a single model that performs all (or some) of thefeatures described herein, for example, with reference to FIGS. 1-9.

At 1002, data is obtained. The data may be obtained by a monitoringprocess over a monitoring interval. The data may be obtained while thepatient is automatically enteral fed by an enteral feeding controller.The data may be obtained from one or more components 202A-F.

The patient may be enteral fed according to a baseline feeding profile,which may include a target nutritional goal, as described herein.

One or more of the following exemplary data may obtained:

-   -   Output of sensors located on a feeding tube positioned for        enteral feeding of the patient. The output of the sensors may        include an indication of an estimated amount of fluid in the        lung(s) of the patient, and/or an indication of an estimated        spontaneous movement of the diaphragm of the patient. Additional        details are described for example, with reference to U.S. patent        application Ser. No. 16/000,922, and/or International Patent        Application No. IB2017/057702.    -   Ventilation-related parameters denoting adjustable settings of        the mechanical ventilator that automatically ventilates the        patient, for example, rate of ventilations (e.g., per minute),        tidal volume, percent of oxygen delivered, and the like.        Additional details of exemplary adjustable ventilation        parameters are described, for example, with reference to U.S.        patent application Ser. No. 16/000,92.    -   Fluid-related parameters denoting actions taken and/or        adjustable settings (e.g., of a controller) that affect patient        fluid balance of the patient, for example, administration of        diuretic medication, administration of antidiuretic medication,        administration of intravenous fluid administration, amount of        enteral fluid administration, and type of fluid being        administered. Delivery may be, for example, manually by a nurse,        automatically by the enteral feeding controller, by a medication        dispenser, and/or by an IV fluid controller.    -   Patient-breathing parameter indicating how well the patient is        current breathing, for example, SpO2, and/or other breathing        and/or oxygenation related parameters.    -   Patient-fluid parameter indicating adjustment of fluid balance        of the patient. For example, administration of diuretic        medication, administration of antidiuretic medication, amount of        urine outputted, time of urine output, concentration of urine        output, and amount of fluid in lungs. The patient-fluid        parameters may be obtained, for example, from the EHR, from        sensors (e.g., an IV fluid controller), from urine sensors,        and/or computed from output of the sensors on the feeding tube        (e.g., fluid in lung).

At 1004, a training dataset is created by computing one or more featurevectors. Each feature vector stores (e.g., an indication of) theobtained data elements, including features computed from the output ofthe sensors located on the feeding tube, the ventilation-relatedparameters, the fluid-related parameters and/or optionallypatient-breathing parameter and/or optionally patient-fluid parameters,for example, as a large vector where each value of each parameter isstored in an element of the feature vector.

Each feature vector may be associated with an indication of time duringthe monitoring interval indicating when the respective data element wasobtained. Alternatively, or additionally, individual data elements areeach associated with an indication of time when the respective dataelement was obtained. The time may be stored as an element of thefeature vector.

Each feature vector may store data elements obtained during a commontime interval, for example, within a 10 minute interval, or othervalues.

At 1006, a model is trained and/or created based on the trainingdataset. The model is adapted to receive current outputs of the sensorslocated on the feeding tube and/or current patient-breathing parametersand/or current patient-fluid parameters and output instructions foradjustment of the mechanical ventilator and/or fluid balance. Theadjustment of the mechanical ventilator and/or fluid balance may be forobtaining a target patient-breathing parameter (e.g., SpO2 of at least90%, or at least 92%, or at least 95%, or other values) and/or a targetpatient-fluid parameter (e.g., at least 1000, or 1500, or 2000 cc ofurine over 24 hours, or other values).

The model may be trained according to computed correlations between thepatient-breathing parameter(s) and/or the patient-fluid parameter(s)and/or the output of the sensors located on the feeding tube and/or theventilation-related parameters and/or the fluid-related parameters. Thecorrelations may be indicative of which patient-breathing parameter(s)and/or the patient-fluid parameter(s) and/or the output of the sensorslocated on the feeding tube and/or the ventilation-related parametersand/or the fluid-related parameters, alone or in a combination, areassociated with various values of target patient-breathing parametersand/or various values of target patient-fluid parameters.

At 1008, current outputs of the sensors located on the feeding tubeand/or current values of the patient-breathing parameter(s) and/orcurrent values of the patient-fluid parameter(s) are fed into thetrained model. The model outputs instructions for adjustment of themechanical ventilator, and/or the fluid balance. The adjustment may beselected for obtaining (i.e., increasing likelihood of reaching) thetarget patient-breathing parameter and/or the target patient-fluidparameter.

The instructions may be code for automatic execution by the mechanicalventilator and/or other controllers (e.g., enteral feeding controller toadd fluid, IV fluid controller, add instructions to the EHR of thepatient). The instructions may be for manual adjustment of themechanical ventilator and/or actions to be performed, for examplepresented on a display, outputted text instructions, audio instructions,and/or a video. For example, instructions to add a diuretic medication,setting IV fluid rate, and/or set the ventilator.

At 1010, the ventilator and/or fluid balance are adjusted according tothe instructions.

At 1012, one or more of features 1002-1010 are iterated over time. Thereceiving of data of 1002, the creating the dataset of 1004, and thetraining the model of 1006 are iterated over time to update the modelwith responses to the adjustment of the ventilator and/or fluid balance.The model learns the effects of its adjustment decisions, anditeratively improves the decision making ability to reach the targetpatient-breathing parameter(s) and/or the target patient-fluidparameter(s). Features of 1008-1010 are iterated over time for new datavalues using the updated model.

Reference is now made to FIG. 10, which is a flowchart of an exemplarymethod for dynamic adjustment of the baseline feeding for meeting atarget nutritional requirement in view of pauses in feeding and/ordynamic changes to the target nutritional requirement, in accordancewith some embodiments of the present invention. The method describedwith reference to FIG. 10 may include, and/or substitute for, and/or becombined with, features and/or components described with reference toFIGS. 1-9.

It is noted that the model described herein may refer to the model ofFIG. 1, and/or other models described herein, and/or combinationsthereof. The method described with reference to FIG. 10 may beimplemented by components of system 200 described with reference to FIG.2.

Reference is also made to FIGS. 11A-G, which are exemplary graphs of thetarget feeding profile and the baseline feeding profile over a timeinterval of 24 hours, to help understand the method described withreference to FIG. 10. A GUI may be created and presented on a display,for example, as a visual aid for the healthcare provider, to track theactual feedings in comparison to the target feeding profile, based onFIGS. 11A-G.

At 1102, a target nutritional goal for automated enteral feeding of thepatient may be received and/or set. The target nutritional goal maydefine a target accumulation of enteral feeding parameters at an end ofa time interval, for example, in 4, 6, 8, 12, 24 hours, or other timeframes. The target nutritional goal may denote an accumulation ofmultiple enteral feeding parameters to reach at the end of the feedingtime interval, for example, total volume of the enteral feeding, totalnumber of calories to provide to the patient, amount of protein toprovide to the patient, and/or other nutrients to provide to the patient(e.g., define multiple components of the feeding).

The nutritional goal may define a maximal limit for the feeding timeinterval, for example, maximum calories and/or maximum volume and/ormaximum protein over 24 hours.

The target nutritional goal may be provided manually selected by a userand/or automatically computed by code. The target nutritional goal maybe computed, for example, based on a resting energy expenditure (REE)computed based on calorimetry sensors (e.g., as described with referenceto International Patent Application No. IL2017/051271), based onpredictive equations (e.g., Harris-Benedict), by a classifier that isfed parameters associated with the patient feeding and trained on atraining dataset of target nutritional goals of sample patients.

An exemplary process for setting the target nutritional goal is nowdescribed. It is noted that other methods may be used, and/or theexemplary method may be adapted. The process may be executed by a GUIpresented on a display associated with the computing device. An REEcalculation is performed for determining the energy amount the patientis using, in order to determine the target nutritional goal. The REEmeasurement duration is defined. Once the duration is set (e.g., whilepatient is in resting) the measurement is launched. The measurement maybe stopped if the patient is restless or for any other reason. After theduration has elapsed, the REE is calculated from the accumulated VCO₂measurement (e.g., by a CO₂ sensor). The results may be accepted, or ifthey seem erroneous, they may be rejected. Once the REE measurement isaccepted, the food calculator may be launched. If the operator selectsto exit this panel it returns to the regular REE panel. The foodcalculator assists in selecting the optimal feeding material based onthe REE calculation, hidden calories (optional to insert thisinformation by the user), protein calculation and other optional filters(e.g., low fiber). Based on the food calculator's parameters and theselected nutrition, the volume to be delivered (VTBD) and nutritionbasal rate are determined, and may be edited by the user. The user isasked by the GUI to set Max rate (per hour) and Max VTBD (per theduration of the feeding time interval, for example 24 hours), this willenable automatic adjustments in order to compensate for feeding stoptime (e.g., because of reflux events or routine procedures by thecaregivers like bedding, CT scans, surgeries, and the like) and for theresidual loss in the GRV residual bag (which is weighted and/or flow ismeasured by a GRV flow sensor). The GRV mechanism is for decompressingthe stomach in reflux event and/or changes in REE estimations that iswithin the determined range of the user. When VCO₂ exceeds the range set(e.g., threshold changed by +\−15%), an alert is generated. If Max VTBDis defined a new kCAL/day target is automatically computed and proposed,along with a newly derived VTBD and nutrition rate. Once accepted, a newtarget nutritional goal is set. Additional exemplary details aredescribed with reference to International Patent Application No.IL2017/051271.

Food type and/or additives may be selected a-priori by the physician andentered into the computing device based on the offering list presentedto him/her according to the hospital available inventory. In anotherimplementation, an updatable database of feeding materials is embeddedin the computing device, or communicated to the computing device via thehospital system to create a personalized top list of the materials thatfit the patient according to the baseline feeding profile.

Additional details of determining the target nutritional goal aredescribed, for example, with reference to 102, where the term baselinefeeding profile may refer to the target nutritional goal and/or to theterm target feeding profile as described below.

At 1104, a target feeding profile for reaching the target nutritionalgoal at an end of a time interval is defined. The target feeding profilemay represent a rate of feeding. The accumulation of the feedingdelivered at the set rate, at the end of the time interval, mayrepresent the target nutritional goal. For example, the targetnutritional goal is 2400 calories at the end of the next 24 hours, andthe target feeding profile is a feeding rate of 100 calories per hour.

The target feeding profile may be computed for reducing risk of reflux,for example, as described herein.

The target feeding profile may be computed by the model, for example, asdescribed herein.

At 1106, the baseline feeding profile is set. The baseline feedingprofile is set by matching to the target feeding profile. The baselinefeeding profile may be defined over the same time interval of the targetfeeding profile for reaching the target accumulation.

It is noted that the target feeding profile may not necessarily beexplicitly defined. For example, the baseline feeding profile may bedirectly set based on the target nutritional goal without explicitlydefining the target feeding profile. The target feeding profile mayrepresent a conceptual step to help understand the process describedherein. Alternatively, the target feeding profile may be explicitlydefined and used as described herein, for example, when a GUI ispresented on a display to the user, for example, based on the graphsdescribed with reference to FIGS. 11A-G.

At 1108, the patient is enterally fed according to the baseline feedingprofile, for example, as described with reference to 104 and/or 116 ofFIG. 1.

The baseline feeding profile indicates the actual feeding delivered tothe patient. The target feeding profile indicates the desired feedingadministered, which may not necessarily be what is actuallyadministered, as described herein.

Referring now to FIG. 11A, graphs of accumulated feedings (inmilliliters (mL)) over time are depicted. Y-axis 1202 depicts theaccumulated feedings. X-axis 1204 denotes passage of time, from a starttime of zero to an end time of 24 hours. Events 1206 occurring atdifferent points in time that may sometimes cause a pause in feeding aredepicted along x-axis 1204. Examples of events 1206 include: going for aCT scan, tracheal suction, routine position change, and medicationbolus.

Graph 1208 indicates the target feeding profile. Graph 1210 indicatesthe baseline feeding profile. Graph 1212 indicates a conceptual standardfeeding profile which is not dynamically adjusted as described herein,in order to help better understand and differentiate graph 1210.

It is noted that from the start (i.e., 0 hour) until the 6^(th) hour1204A, the target feeding profile 1208 matches the baseline feedingprofile 1210.

Referring now back to FIG. 10, at 1110, the enteral feeding is paused(e.g., by the enteral feeding controller) for one or more pause timeintervals. A gap is formed between the target feeding profile and thebaseline feeding profile during each pause time interval. The gapindicates the difference between the part of the target nutritional goalthat the patient should have received, and the feedings that the patientactually received.

Additional details of pausing the enteral feeding are described, forexample, with reference to 108 of FIG. 1.

Reference is now made to FIG. 11B. At about 6.5 hours, the patient istaken for a CT scan. Feeding is paused for about 2 hours. A gap 1204B isformed between the target feeding profile 1208 and the baseline feedingprofile 1210.

Referring now back to FIG. 10, at 1112, the baseline feeding profile isadjusted to a higher feeding rate than the previous feeding rate of thebaseline feeding profile, which matches the feeding rate of thecorresponding target feeding profile. In other words, the baselinefeeding profile is adjusted to a higher feeding rate than the feedingrate of the corresponding target feeding profile.

The adjustment to the higher feeding profile is performed in an attemptto close (and/or reduce) the gap for reaching the target accumulation atthe end of the time interval.

Optionally, the baseline feeding profile is adjusted to a definedmaximal feeding rate. The maximal feeding rate may indicate a maximumfeeding rate which is not to be exceeded by the feeding controller. Themaximal feeding rate may be computed according to a risk of likelihoodof future reflux event below a requirement for no scheduled and/orpredicted reflux-related parameters, for example, by the model.Alternatively or additionally, the maximal feeding rate may be manuallyselected by a user (e.g., physician). The maximal feeding rate may beset according to a feeding rate tolerated by an enterally fed patientfor which risk of reflux events are low (i.e., no expected upcomingchange of patient position that increase risk of reflux event), forexample, computed by the model and/or manually set by a user.

Reference is now made to FIG. 11C. After about 8.5 hours, when thepatient returns from the CT scan, the feeding is resumed. At 1204C, thefeeding rate of the baseline feeding profile 1210 is adjusted to ahigher rate than the feeding rate of the corresponding target feedingprofile 1208. The feeding rate of the baseline feeding profile 1210 maybe adjusted to the maximal feeding rate. It is noted that for comparisonpurposes, the standard feeding protocol 1212 is simply resumed to itsprevious rate after the pause. It can be visually seen that the standardfeeding protocol 1212 will never close the gap with the target feedingprofile 1208, while the higher rate of the baseline feeding profile 1210will eventually intersect (i.e., close the gap) with the target feedingprofile 1208.04

Reference is now made to FIG. 11D. Multiple pauses in feeding aredepicted 1204D-F. After each pause, the feeding is resume by adjustingthe baseline feeding profile 1210 to the higher rate (e.g., the maximalrate) in an attempt to eventually close the gap with the target feedingprofile 1208. The gap is successfully closed as shown. Referring nowback to FIG. 10, at 1114, the gap between the target feeding profile andthe baseline feeding profile may be monitored. The closing of the gapmay be detected. The closing of the gap may be defined, for example, bya requirement, such as a range of values that define when the gap isclosed. For example, the gap may be closed when the actual feeding iswithin about 100 calories, or about 50 calories, or other values, of thetarget nutritional goal. It is noted that an exact match (i.e., gap ofzero) is not necessarily required.

At 1116, when the gap has been determined as being closed, the feedingrate of the baseline feeding profile may be adjusted down (i.e.,reduced), to match the feeding rate of the target feeding profile.

Reference is now made to FIG. 11E. At 1204G, when the gap betweenbaseline feeding profile 1210 and target feeding profile 1208 has beenclosed, the feeding rate of the baseline feeding profile 1210 may beadjusted back to match the feeding rate of the target feeding profile1208.

Referring now back to FIG. 10, at 1118, one or more features describedwith reference to 1102-1116 are iterated. The features 1102-1116 may beiterated over the time interval, and/or may be iterated over multiplesequential time intervals.

Optionally, the model is updated based on the iterations described withreference to 1102-1116, for example, as described herein and/or withreference to 110 of FIG. 1.

In an exemplary iteration, at 1102, the target nutritional goal isdynamically adjusted, for example, due to changes in the patient'smedical condition. Such changes in the medical conditions may translateinto additional calories, and/or change in mix of nutrition (e.g.,higher percentage of protein) and/or a reduction in calories. At 1104,the target feeding profile for reaching the dynamically adjusted targetaccumulation of enteral feeding parameters at the end of the timeinterval is dynamically adjusted. At 1106, the baseline feeding profileis dynamically adjusted to the adjusted target feeding profile, tocorrespond to the dynamically adjusted target nutritional goal.

Reference is now made to FIG. 11F. At 1204H, the patient's REE increasedsignificantly, triggering an increase in the target nutritionalrequirement to reach at the end of 24 hours. The original target feedingprofile 1210 is dynamically adjusted, creating an adjusted targetfeeding profile 1220, according to the updated target nutritionalrequirement. The feeding rate of the baseline feeding profile 1208 isadjusted to match the adjusted target feeding profile 1220.

Reference is now made to FIG. 11G. At the end time interval of 24 hours1204I, the dynamically adjusted baseline feeding profile 1208 thatmatches the dynamically adjusted feeding profile 1220 achieved thedynamically adjusted target nutritional goal at the end of 24 hours of1820 mL (e.g., within a tolerated error range). The dynamic targetnutritional goal was met, despite pauses in feedings, and/or despitechanges in patient nutritional requirements (e.g., increased REE). Incomparison, without the dynamic adjustment of the nutritional feedingrequirement, the original target feeding profile 1210 would meet theoriginal nutritional target of 1560 mL, which has not kept up withchanging patient nutritional requirements. In yet another comparison,without the dynamic adjustment of the feeding rate to compensate forpauses in feeding 1212, only 1090 mL of feeding would be provided to thepatient.

Various embodiments and aspects as delineated hereinabove and as claimedin the claims section below find computed support in the followingexamples.

EXAMPLES

Reference is now made to the following examples, which together with theabove descriptions illustrate some embodiments in a non limitingfashion.

Reference is now made to FIG. 12, which is a graph presentingexperimental results of an experiment performed by the Inventors forassessing ability of a system that automatically enterally feeds apatient to meet a target nutritional feeding requirement in view ofreflux events and/or pauses in the enteral feeding, in accordance withsome embodiments of the present invention. The graph depicts time on thex-axis. The time interval is from 10 AM to 7 AM the next day. The totalvolume administered is depicted on the y-axis.

Curve 1310 denotes the target feeding profile, which indicates thedesired feeding rate, for meeting a target nutritional requirement 1304E(representing 100% of the nutritional goal at the end the timeinterval), as described herein.

Curve 1308 denotes the baseline feeding profile, indicating the actualfeeding rate administered to the patient.

Between about 10 AM and 8:30 PM (denoted by element 1304A), baselinefeeding profile 1308 matches target feeding profile 1310.

Between about 8:30 PM and 11:30 PM (denoted by element 1304B), theenteral feeding is paused due to the patient being taken to a medicalprocedure. A gap is formed between baseline feeding profile 1308 andtarget feeding profile 1310.

Between about 11:30 PM and 5:30 AM (denoted by element 1304C), thefeeding rate of baseline feeding profile 1308 is increased, optionallyto the maximum value, denoting a compensation rate 1308A thatcompensates for the long pause in feeding of 1304B. It is noted thatfeeding is paused for short periods of time during reflux and/orauto-GRV 1304C and then resumed. Reflux events are shown as dots markedby element 1352 at times corresponding to 1304C. Reflux events may beused to update the model as described herein, and/or for adjusting thefeeding rate of the baseline feeding profile, as described herein.

At about 5:30 AM (denoted by element 1304D), the gap between baselinefeeding profile 1308 and target feeding profile 1310 has been closed.

From about 5:30 AM until 7:00 AM (denoted by element 1304E), the feedingrate of baseline feeding profile 1308 is reduced to match the rate oftarget feeding profile 1310.

The 100% target 1304E at the end of the time interval is reached at theend of the time interval (within a tolerance range).

Graph 1350 shows the rate of water administered to the patient, at aconstant rate over the time interval.

The following summarizes the experimental results. The targetnutritional requirement to reach by the end of the time interval (i.e.,“Total nutrition prescribed”) is 979 ml. Target feeding profile 1310 isset to meet the target nutritional requirement. The feeding actuallyadministered to the patient (i.e., “Total nutrition”) is 978 ml,representing a feeding efficiency of 99.90%. Baseline feeding profile1308 represents the dynamically adjusted feeding rate of the feedingsactually administered to the patient. It is noted that the feedingefficiency without compensation, which would otherwise be delivered ifthe baseline feeding profile is simply paused, without adjusting therate for compensating for gaps, is 84.45%. Of note is that 115 ml ofwater were administered over the time interval.

Reference is now made to FIG. 16, which is a flowchart of an exemplarymethod of operational flow of an automated patient feeding device, inaccordance with some embodiments of the present invention. Featuresdescribed with reference to FIG. 16 may be combined, integrated,replaced, and/or added to other features of other systems and/or methodsdescribed herein, and/or described with reference to other FIGsdescribed herein.

At 1902, the proper position (i.e., target location when feeding) of thefeeding tube within the patient is confirmed, for example, as describedherein, for example, using a GUI described with reference to FIG. 13.

The GUI may depict a schematic of an outline of a body of the patientincluding an indication of a current location of the feeding tube. Thetube is maneuvered until the GUI image confirm the correct position, forexample, by a message, color coding, and/or other indication. Tubelocation may be detected, for example, using impedance sensors todetermine location within the stomach, and/or relative to the LES.

Optionally, feeding is prevented from automatically starting when thefeeding tube is detected as being outside the target location. Forexample, additional features of the GUI for starting and/or selectingfeedings are not presented until the tube is located at a target zone.The confirmation of the proper location of the feeding tube may indicatea start of the feeding process.

The location of the feeding tube may be dynamically monitored, and fedas a parameter (e.g., gastric parameter) into one or more of the modelsdescribed herein. For example, location of the feeding tube maycorrelate with risk of reflux.

At 1903, one or more feeding formulas (also referred to herein asformulations) may be selected, for example, from a list of candidateand/or available formulas. The list of candidate formulas may bemanually and/or automatically selected, for example, based on REE and/orother patient parameters (e.g. as described herein, such as urineoutput), such as the medical state of the target patient (e.g., based onmanually entered data, and/or data obtained from the electronic healthrecord). For example, to provide 100% (or near 100%, for example, withina tolerance range, for example, within 90% or 95%) of the targetnutritional goal of the patient. Supplemental protein may be selected toprovide additional protein when the selected nutritional formula islacking sufficient protein to meet the nutritional goal of the patient.The nutritional goal may be set according to the REE and/or otherpatient parameters, for example, using predictive equations as describedherein.

The feeding formulas may be selected using an interactive graphical userinterface (GUI), which may present values of the REE and/or otherpatient parameters.

The selected feeding formulas and/or the REE and/or other patientparameters may be fed into the model(s) described herein, for example,for computing adjustments to the baseline feeding profile and/orpredicting risk of reflux event, and/or other predictions as describedherein.

Optionally, one or more interactive GUIs are presented on a display, andused by a user for setting up the baseline feeding profile and/or targetfeeding profile. For example, the GUI may present additional patientdata, for example, obtained from sensors, and/or patient data presentedin the electronic medical record. The additional patient data may beused to compute the REE and/or baseline feeding profile and/or targetnutritional profile, and/or for selecting from available feedingformulations, as described herein.

Initial setting data (e.g., REE, initial patient status) may be received(e.g., from sensor data, manually entered by the user, from thepatient's electronic health record). The initial setting data may beused to set the feeding policy, as described herein.

Optionally, the daily nutrition requirement (e.g., the targetnutritional goal) for the patient is as calculated from according to theREE computed based on measurements of CO₂ expiration level and/or O₂consumption, which may be measured by sensor(s).

The calculation (e.g., of the baseline feeding profile and/or targetfeeding profile) may include available suitable nourishment(s) (e.g., instock), optionally optimal nourishment, and/or extra protein and/or thehidden calories carried by medication. The user may select the feedingformulations to be provided to the patient from a list of availablesuitable feeding formulations (e.g., presented in the GUI), such as theavailable formulations that are closest in matching to the caloric needsand/or protein requirements of the patient. Additional water for propersystem operation may be automatically computed and added to the feedingschedule (e.g., the baseline feeding profile and/or target feedingprofile). Supplemental protein may be presented for selection (e.g.,brand and/or quantity), for example, based on protein deficiency in thepresented feedings. Supplemental protein may be selected from dedicatedprotein sources which may be added to the selected feeding formulation.Optionally, the presented nourishments are filtered according to thepatient's medical condition (e.g., manually entered by a user and/orextracted from the electronic health record of the patient), forexample, certain nourishments are excluded for diabetics and/or thosewith cardiovascular disease.

Optionally, the most suitable nourishment (i.e., feeding formulation)available is presented in the GUI for selection, and/or selected, basedon protein requirements while matching the measured REE measured andsupplemental protein to add to the feedings provided to the patient.“Hidden” calories carried by the extra protein and/or medication arecompensated. When medications are prescribed to the patient, thecalories the patient receives from the medication itself (referred toherein as hidden calories) are considered as part of the overallbaseline and/or target feeding profile, included in the overall energybalance for the patient. The baseline feeding profile and/or targetfeeding profile may be set according to the selected available feedingformulations, supplemental protein, and/or hidden calories from thesupplementation protein and/or medications.

At 1904, one or more feeding policy settings are defined, optionallyusing a designed user interface, for example, an interactive GUI, asdescribed herein. Exemplary feeding policy settings include: rate offeeding, baseline feeding profile, target feeding profile, targetnutritional goal, nutritional composition of feeding (e.g., water,protein), and/or type of feeding, as described herein.

Optionally, the user may use interactive GUIs, which may direct the userfor setting the proper values for the feeding program (e.g., selectingparameters for the baseline feeding profile and/or target feedingprofile).

Optionally, based on the REE (e.g., presented in the GUI), the feedingrate (e.g., in kiloCalories per day (kCal/day)) is computed, forexample, the baseline feeding rate and/or target feeding profile is inthe range of about 15-100 milliliters per hour (ml/hr), or about700-2500 kCal/day.

Feeding may start gradually (e.g., as selected by the operator and/orpredefined and/or automatically computed), for example, during the first4 days, the following percentage of the target nutritional goal and/orbaseline feeding profile and/or target feeding profile is set: 30% forthe first day, 50% for the second day, 70% for the third day, and 100%for the fourth day, are set. It is noted the percentages are examples ofvalue and not necessarily limiting.

The target nutritional requirements may be matched (e.g., exactly and/orapproximately within a tolerance range, optionally a closest but notnecessarily exact match) to the existing nutritional inventory availableto the feeding facility (e.g., stored in the hospital). Optionally extraprotein is added (e.g., manually by the user and/or automaticallyselected by code) depending on patient medical status.

Optionally, maximum feeding rates (i.e., used to compensate when thebaseline feeding profile falls below the target feeding profile, asdescribed herein) may be defined, for example, as straight broken lines1606 described with reference to FIG. 15A. The maximal feeding ratedefines a pumping rate (i.e., feeding rate) should never be exceeded,for example, when compensating for feed halts due to reflux, treatments,procedures, and/or GRV events, as described herein. A final setupinspection may be performed. Triggering a START command (e.g., pressinga start icon on the GUI) initiates the fully automatic feeding, asdescribed herein.

Final initial settings of the feeding controller (e.g., baseline feedingprofile and/or target feeding profile and/or target nutritional goal)for feeding the patient may be presented, for example, within the GUI.Exemplary settings include maximum rate and/or maximum volume to bedelivered (VTBD). The VTBD may be defined for a time interval, forexample, 6 hours, 12 hours, 24 hours, or other values. The GUI maypresent a visual snapshot of the feeding plan (e.g., baseline feedingrate, target feeding profile, nutritional target, maximal adjustmentrate) for review by the healthcare provider.

The following are exemplary equations representing relationships ofvariables described herein in mathematical terms:

$\left. {{{{CT}\left\lbrack \frac{kCal}{day} \right\rbrack} = {{{caloric}\mspace{14mu} {dailytarget}} = {{{REE} \cdot \left( {\% \mspace{14mu} {plan}} \right)} - {{hidden}\mspace{14mu} {calories}}}}}{{V\; T\; B\; {D\left( {{volume}\mspace{14mu} {to}\mspace{14mu} {be}{\mspace{11mu} \;}{delivered}} \right)}} = {\frac{CT}{{caloric}\mspace{14mu} {value}\mspace{14mu} {of}{\mspace{11mu} \;}{nutrient}} \cdot \frac{duration}{24}}}} \right)\; {\quad\mspace{11mu} {{\lbrack{ml}\rbrack \mspace{20mu} {V\; T\; B\; D\mspace{14mu} {rate}}} = {{{\frac{V\; T\; B\; D}{duration}\left\lbrack \frac{ml}{hr} \right\rbrack}\mspace{20mu} \max {\mspace{11mu} \;}{rate}} = {{{basal}\mspace{14mu} {rate} \times 1.75\mspace{14mu} ({defoult}){Max}\mspace{14mu} V\; T\; B\; D\mspace{14mu} {rate}} = {V\; T\; B\; D\mspace{14mu} {rat} \times 1.2\mspace{14mu} \left( {{or}\mspace{14mu} {by}{\mspace{11mu} \;}{physician}\mspace{14mu} {instruction}} \right)}}}}}$

CT denotes the caloric target (e.g., daily), i.e., target nutritionalgoal at the end of the time interval.

REE is as described herein

% Plan denotes the percentage of the maximal total nutritional goal thatis gradually increased per day, as described herein.

Hidden calories denotes the calories in medications that are excludedfrom the target nutritional goal, since the calories will be provided inthe prescribed medications.

Caloric value of nutrient denotes the calories in the selected feedingformulation.

Duration denotes a certain time interval from a daily 24 hours, overwhich the target and/or baseline feeding profile are defined.

It is noted that overall values may be used to compute the maximumfeeding rate (i.e., max rate) from the basal rate (i.e., the baselinefeeding rate), for example, from the range of 1.5-2, or 1.7-1.9, or 1.6,1.7, 1.8, 1.9, or other values.

It is noted that overall values may be used to compute the maximum VTBDrate (i.e., max VTBD rate) from the VTBD rate (i.e., the baselinefeeding rate to reach the defined VTBD target at the end of the timeinterval), for example, from the range of 1.1-1.4, or 1.1-1.3, or 1.1,1.3, 1.4, 1.25, or other values.

The setup procedure may include one or more of the following features,for example, as described herein:

-   -   Present interactive popup screens (e.g., GUI) for parameters        initiation, optionally based on sensors input and/or data stored        in the electronic health record.    -   Identifying one or more best protein matching feeding formulas        (e.g., available at the feeding site).    -   Calculating the extra protein to be provided as a supplement to        the patient. The supplemental protein may be included in the        baseline feeding profile and/or the target feeding profile.    -   Define a flexible feeding schedule including on/off feeding        pause periods.

An Exemplary target feeding profile (i.e., feeding plan) computed forthe target patient may include one or more of:

-   -   Display of REE information (e.g., dynamically updated in real        time)    -   Rate ranges e.g. 15-100 mL/hour, and and kCal/day 700-2500        kCal/day    -   Define gradual increase in percentage of target nutritional goal        defined per time interval (e.g., per day), for example, define 4        day percentage escalation and fixed percentage thereafter—e.g.        30% on day 1, 50% on day 2, 70% on day 3, 100% on day 4, and        thereafter 100%.    -   Selection of feeding formulations (i.e., nutrients) from a list        of in-stock formulations (e.g., the hospital). Optionally, when        a certain formulation has run out, another available formulation        that best matches to the patient requirements (as described        herein) is presented and/or selected.    -   Target feeding profile and/or baseline feeding profile takes        into account one or more of: patient weight, protein factor        (e.g., increased protein requirements over what is available in        standard formulations) and/or protein tolerances, (+−grams) of        protein that don't need to be compensated for (e.g. +5 g, −10        g).    -   Intermittent mode (i.e., on/off) and/or number of meals and/or        interval (e.g., at top rate range, controlled by reflux        detection).    -   Water per required urine output level.    -   Press start fully automatic mode.

At 1906, feeding is commenced. The patient is automatically fed usingthe feeding tube, as described herein. The feeding may gradually beincreased as set. Alternatively, when feeding has been paused (e.g., dueto a procedure, as described herein), feeding commences.

The feeding may be performed in a fully automated and/or semi-automatedmanner. For example, instructions are generated for controlling a pumpand/or valve (e.g., pinch valve) to deliver and/or pause feedings,without necessarily requiring intervention by a human user.

Feeding may be automatically provided to the patient according to one ormore of the following features, for example, as described herein:

-   -   Closed loop operation (i.e., fully automatic).    -   Automatic compensation of the feeding rate to catch-up with feed        losses, for example due to GRV events and/or feed halting due to        reflux or result of treatment.    -   Water flow rate (i.e., supplemental water provided to the        patient) adjusted according to urine output, for example,        matching the urine output, and/or increased to increase urine        output, and/or decreased to decrease urine output.    -   Matching (e.g., within a tolerance range) the instantaneous REE        to the feeding rate, for example, at preassigned intervals i.e.        update the feed flow correspondingly.

At 1908, one or more parameters are monitored, as described herein.Exemplary monitored parameters include one or more of: urine status,REE, CO₂ measurements (e.g., from patient expiration), O₂ measurements(e.g., from patient inhalation), feeding plan (e.g., baseline feedingprofile, target feeding profile), and/or showing one or more feedingtube related indicators: reflux, tube position and/or GRV, gastricreflux-related parameters, gastric reflux events, other feedingparameters and/or clinical parameters, as described herein. Theparameters may be fed into the model, as described herein. Discrepanciesmay be detected, as described herein.

Optionally, a real time REE is computed based on the monitored CO₂and/or O₂ measurements. The real time REE may be fed as input fordynamically adjusting the baseline feeding profile, for example, the REEis fed into the model, as described herein.

Optionally, a real time indication of the parameters is presented withinthe GUI, providing a working screen presenting the real time status ofthe patient.

Optionally, one or more graphs are created and presented (e.g., within aGUI) based on the monitored parameters, for example, as described withreference to FIGS. 15A-B.

The exemplary following features may be automatically implemented duringthe fully automated feeding procedure, and/or during the monitoring ofthe parameters:

-   -   Every set time interface (e.g., half hour, hour, or other value)        the actual energy expenditure may be measured, for example,        based on sensors that estimate CO₂ and/or O₂ values, as        described herein.    -   The actual feedings (e.g., volume, weight) to be delivered may        be adjusted automatically accordingly (e.g., the baseline        feeding profile is adjusted). For example, at an initial time        interval the target feeding profile and/or baseline feeding        proile is initially set to 30 [kCal/hr], for example, based on        REE measurements and/or output of the model. At a subsequent        time interval, the actual measured REE is 33 [kCal/hr].        Instructions for adjustment of the baseline feeding profile        (i.e., feeding pump and/or feeding controller) according to the        updated value of the REE may be generated, for example, to        increase the feeding rate to match the updated REE value. The        actual feeding by the pump and/or controller is correspondingly        adjusted to the adjusted baseline feeding profile.    -   Feeding halts and/or GRV, which represent actual losses of        feeding (i.e., GRV) and/or loss of potential feeding (i.e., time        during which feeding has not been delivered due to halts) are        compensated for automatically, for example, by increasing the        feeding rate to the maximal defined rate, in order to close the        gap between the actual feedings (i.e., baseline feeding profile)        and the target feeding profile, as described herein.    -   Present and dynamically update in real time, a snap review        monitor screen (e.g., GUI) that presents real time values of one        or more parameters, for example, REE, urine rate, and the like.        The parameters may be used to compute and/or adjust the baseline        feeding rate.    -   Present warnings in the GUI, for example, as pop-up windows. For        example, when values of parameters deviate from a defined target        zone.    -   When feeding and/or water contains are exhausted or about to        become empty in the near future. Best matching replacements may        be presented in the GUI for selection.    -   Container replacement may be followed by an automatic priming        cycle.

Optionally, one or more alarms and/or intermitted indications aregenerated, for example, when values fall out of defined normal and/orsafe values. The alarms and/or indications may be presented within theGUI, such as showing current state (e.g., normal, safe, and/ornot-normal and/or risky), for example, as described with reference toFIG. 13. Exemplary alarms and/or indications include one or more of:REE, Urine, GRV, reflux, replace drainage bag, nutrient currently beingfed to patient (e.g., type), warning when current nutrient will berunning out (or has run out), and water runout warning.

At 1910, one or more corrective procedures are performed. The feedingpay be paused during the corrective procedure. For example, therefluxing material is evacuated from the patient, and/or the baselinefeeding profile (and/or other feeding policy settings) is adjusted, asdescribed herein. In another example, feeding is paused while thepatient is taken for imaging, to clear the tracheal tube, and/or forother procedures as described herein.

At 1912, features described with reference to 1902-1910 are iterated,for example, over a time interval while the patient is enterally fed,until the patient resumes eating without a tube.

The actual energy expenditure of the patient may be continuouslymeasured, for example, based on the REE and/or other parameters asdescribed herein. The baseline feeding profile may be dynamicallyadapted, as described herein.

Reference is now made to FIG. 13, which is a schematic of an exemplaryGUI 1402 presenting an overall patient status, in accordance with someembodiments of the present invention. GUI 1402 provides a fast patientstatus observation monitor screen. GUI 1402 is designed to provide asnap shot and/or quick view of the patient status. GUI 1402 may bedisplayed on a monitor as a default image capable of alerting the caretakers in case of deteriorating clinical indicators.

GUI 1402 may be presented as a dashboard, with one or more of thefollowing exemplary regions:

-   -   Vertical bar(s) 1404 indicating, for example, the baseline        feeding (i.e, representing actual feeding) as a percentage of        the target feeding (e.g., in terms of real time rate),        accumulated baseline feeding profile (i.e., actual amount of        feedings provided to the patient) as percentage of target        nutritional goal, i.e., how much of the total daily target the        patient has already received, and accumulated baseline feeding        profile as percentage of target feeding profile—which represents        the patient's nutritional deficit which may be restored by        increased feeding rates as decried herein. The vertical bar is        one example implementation, for example, the percentage may be        presented as a value, as a bar graph, and/or color coded to        indicate whether the percentage is significant or not (e.g.,        green when within an allowed tolerance, and red when the        percentage falls outside the allowed tolerance indicating        insufficient feeding).    -   Graphic 1406 indicating current location of the feeding tube        and/or indication of whether the tube is in the correct place.        The graphic may include the esophagus and stomach, and a tube        positioned within the esophagus and/or stomach according to tube        location (e.g., based on impedance sensor measurements located        on the distal end region of the feeding tube).    -   Circular dials 1408 presenting key parameter, for example, GRV,        reflux, urine flow, metabolism, and/or other parameters        described herein. Dials 1408 provide an indication of whether        the values of the parameters are within normal, for example, a        pointer within a vertical sector indicates normal values, and        deviation clockwise or anti-clockwise indicates abnormal values.        It is noted that dials are examples, for example, numerical        values may be presented and/or the dials and/or values and/or        other graphical elements may be color coded to indicate whether        the respective values are within defined targets (e.g., normal,        healthy, desired values) or not.

It is noted that values depicted by GUI 1402 may be fed into themodel(s) as described herein. For example, location of feeding tube,metabolism, and urine flow may be fed as additional gastric parameters(over the reflux, GRV and/or actual feedings) into the model forpredicting risk of reflux and/or adjusting the feeding rate.

Reference is now made to FIG. 14, which is a graph 1502 of exemplarytypical CO₂ production rates (in milliliters per minute), in accordancewith some embodiments of the present invention. The values of graph 1502may be indicative of REE values, which may be used to select and/oradjust patient feeding rates, for example, as described herein. Valuesof graph 1502 have been obtained for a real patient.

Reference is now made to FIG. 15A-B which includes a graph 1602A and1602B depicting an exemplary nutritional daily chart for a patient,summarizing exemplary important nutritional status of the patient, inaccordance with some embodiments of the present invention. Graph 1602 ofFIG. 15B provides more details over graph 1602A of FIG. 15A. Followingthe feeding progress track is an important early indicator ofapproaching organ crash. Portions of graphs 1602A-B may be computedand/or adjusted, for example, as described herein, for example, based onthe model described herein.

1604 is a y-axis indicating the amount of feeding relative to time (onx-axis 1616) relative to a Calculated Feeding target (e.g., in ml/day orkcal/day), as described herein.

1606 indicates an example of equal maximal feeding rate lines, definingmaximal feeding rates, for example, as described herein.

1608 is a marked region (e.g., shaded, color coded, marked with anindication such as arrow and/or text) between expected feed trajectory1610, i.e., target feeding profile (e.g., computed by H-B and/or Weirand/or other predictive equations, for example, as described herein) andactual feeding trajectory 1612, i.e., baseline feeding profile (e.g.,computed by Weir, for example, as described herein). Feeding halts aredepicted as straight segments of curve 1612 indicating no feeding hasbeen provided to the patient. Feeding halts may occur, for example,events 1650 and 1652 depicted in FIG. 15B. Feeding halts may be due to,for example, due to treatments performed on the patient and/or medicalprocedures (e.g., x-ray). Current deficiency 1614 is computed, for avalue along the current time axis 1616, as a difference between thecorresponding values of curve 1610 and 1612. Shaded region 1608 mayrepresent a buildup of nutritional deficiency occurring over the feedingtime interval. The size of shaded region 1608 indicates the amount ofdeficiency, for example, indicative of risk of organ collapse.

The feeding target and/or the corresponding feeding trajectory may becalculated according to the equation to match the patient's REE & EEE(e.g., as described herein) and/or adjusted and/or computed using themodel described herein.

Graph 1602 of FIG. 15B shows an example of: a halt event 1650, a haltevent with GRV 1652, basal rate 1654, max (i.e., recovery) feeding rate1656, actual feed trajectory 1658, expected feed trajectory 1660, longhalt event 1662, day end deficiency 1664, and calculated feeding target1666.

Reference is now made to FIG. 15C, which is a graph depicting an exampleof a halt in feeding 1670, which is compensated for by automaticallyincreasing the feeding rate (e.g., instructing a pump to increase thepumping rate) 1672 (i.e., adjustment to the baseline feeding profile) toreach a defined basal rate (i.e., target feeding profile) 1674, inaccordance with some embodiments of the present invention. Once thedeficiency between actual feeding (based on the baseline feedingprofile) and target feeding profile substantially match, the baselinefeeding profile is set to the target feeding profile 1676. For example,the basal rate 1674 (i.e., target feeding profile) is initially set to40 ml/hour (e.g., as described herein). The feeding is halted 1670 for15 minutes (e.g., for reasons as described herein), which creates adeficit in feeding (i.e., gap between baseline feeding profile andtarget feeding profile). The baseline feeding profile is adjusted to 80ml/hour 1672 to close the deficit. The baseline feeding profile isadjusted to match the target feeding profile 1676 of 40 ml/hour.

Reference is now made to FIG. 15D, which is a graph depicting an exampleof a change in the target feeding profile, in accordance with someembodiments of the present invention. The original basal rate 1870(i.e., target feeding profile) is set to 60 ml/hour. At 10:00 Am, thebasal rate increases 1872 to 72 ml/hour (i.e., target feeding profile isadjusted). The baseline feeding profile, which is set to match thetarget feeding profile, is adjusted accordingly. It is noted that theincrease in VTBD 1874 for the 24 hour time interval increases from 1440to 1610 ml, i.e., increase of 170 ml.

It is noted that the feeding rate may be increased to a higher rate, upto the maximal rate. The maximal rate may be selected according tolikelihood of the patient refluxing the enteral feeding being below athreshold, for example, computed by the model(s). The maximal rate maybe set as the maximal rate that the patient can tolerate withoutrefluxing, optionally determined based on feeding rate that the patientcan tolerate without refluxing and/or the maximal volume that thepatient can tolerate without refluxing.

Reference is now made to FIG. 17, which is a schematic that graphicallydepicts correlations between parameters and reflux events, in accordancewith some embodiments of the present invention. Schematic 1802 depicts astrong correlation between parameter denoted x[1] and a reflux event,via a high defined peak. Schematic 1804 depicts a medium correlationbetween parameter denoted x[2] and a reflux event, via a more spread outbut still defined and medium high peak. Schematic 1806 depicts a weakcorrelation between parameter denoted x[3] and a reflux event via aspeak out and not well defined and low peak.

Patient clinical parameters for which a strong reflux correlation hasbeen calculated (e.g., based on accumulated data), as described herein,for example, with reference to FIG. 5 are identified. For example,correlation values above a threshold (e.g., 0.7, 0.8, 0.9, or othervalue) represent strong correlation between a parameter and refluxevent, mathematically denoted as ρ[1]>0.8, ρ[2]>0.8 . . . for parametersx[1], x[2] . . . .

The parameters highly correlated with reflux may be accounted for (e.g.,automatically, and/or manually by the user). For example, when theparameter associated with patient maneuvering has been shown to beinduce reflux (i.e., via high correlation factor above the threshold)feeding may be halted (e.g., manually and/or automatically) beforemaneuvering the patient to reduce the possibility of reflux.

Detecting the correlation between reflux incidents and the patient'sparameters and/or treatment may be performed as described herein, and/orfor example, as follows: Whenever a reflux event is observed (denotedY=1) the said parameter levels are recorded. As time passes theaccumulated data indicate the level of influence the parameters have onthe reflux.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

It is expected that during the life of a patent maturing from thisapplication many relevant computing devices and enteral feedingcontrollers will be developed and the scope of the terms computingdevices and enteral feeding controllers are intended to include all suchnew technologies a priori.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”. This termencompasses the terms “consisting of” and “consisting essentially of”.

The phrase “consisting essentially of” means that the composition ormethod may include additional ingredients and/or steps, but only if theadditional ingredients and/or steps do not materially alter the basicand novel characteristics of the claimed composition or method.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

The word “exemplary” is used herein to mean “serving as an example,instance or illustration”. Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference.

In addition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting. In addition, any priority document(s) of this applicationis/are hereby incorporated herein by reference in its/their entirety.

What is claimed is:
 1. A system for automated enteral feeding of apatient, comprising: at least one hardware processor executing a codefor: computing a target nutritional goal to reach at an end of a timeinterval based on a basal metabolic rate of a patient obtained over aportion of the time interval, wherein the target nutritional goalcomprises a volume to be delivered (VTBD) by the end of the timeinterval corresponding to a target amount of energy expenditure of thepatient over the time interval computed according to the basal metabolicrate; computing a target feeding profile defining a target feeding ratefor enteral feeding of the patient by an automated enteral feedingdevice for reaching the VTBD by the end of the time interval;continuously monitoring the real time energy expenditure of the patientover the time interval based on continuous measurements of the basalmetabolic rate; dynamically adapting the target nutritional goal andcorresponding VTBD to compute a maximum VTBD (max VTBD) to reach at theend of the time interval according to dynamic adaptations of themonitored real time energy expenditure of the patient; dynamicallyadapting the target feeding rate and the corresponding target feedingprofile for a remaining portion of the time interval for reaching themax VTBD by the end of the time interval, wherein the target feedingprofile tracks changes of the basal metabolic rate.
 2. The system ofclaim 1, wherein a feeding rate for reaching the maximal VTBD iscomputed as about 1.2*the feeding rate for reaching the VTBD.
 3. Asystem for automated enteral feeding of a patient, comprising: at leastone hardware processor executing a code for: computing a target feedingprofile denoting a target enteral feeding of the patient by an automatedenteral feeding device for reaching a target nutritional goal at an endof a time interval; defining a baseline feeding profile of a patient bymatching to the target feeding profile, wherein the patient isautomatically fed by the automated enteral feeding device according tothe baseline feeding profile for reaching the target nutritional goal;pausing or slowing down the enteral feeding by the automated enteralfeeding device for at least one pause time interval; wherein a feedingdeficiency is formed between the target feeding profile and the baselinefeeding profile during each pause time interval; and adjusting thebaseline feeding profile is to a higher feeding rate that is higher thana feeding rate of the corresponding target feeding profile to compensatethe feeding deficit for reaching the target nutritional goal at the endof the time interval.
 4. The system of claim 3, wherein the higherfeeding rate is a defined maximal feeding rate.
 5. The system of claim4, wherein the maximal feeding rate is selected according to likelihoodof the patient refluxing the enteral feeding being below a threshold. 6.The system of claim 4, wherein the maximal feeding rate is computed atabout 1.75*a feeding rate defined by the target feeding profile.
 7. Thesystem of claim 3, further comprising a code for: detecting when the gapbetween the target feeding profile and the baseline feeding profile hasclosed; and reducing the baseline feeding profile to match the targetfeeding profile.
 8. The system of claim 3, further comprising a codefor: dynamically adjusting the target nutritional goal; dynamicallyadjusting the target feeding profile for reaching the dynamicallyadjusted target nutritional goal at the end of the time interval; anddynamically matching the baseline feeding profile to the adjusted targetfeeding profile for reaching the dynamically adjusted target nutritionalgoal.
 9. The system of claim 3, wherein the target nutritional goal isdynamically adjusted according to dynamic values of a computed restingenergy expenditure (REE) of the patient, wherein the target nutritionalgoal is matched within a tolerance range to energy requirements of thepatient determined according the REE.
 10. The system of claim 9, whereinthe adjustment to the baseline feeding profile comprises an adjustmentto the baseline feeding profile dynamically matched to the adjustedtarget feeding profile.
 11. The system of claim 3, further comprising acode for: presenting within an interactive graphical user interface(GUI) on a display, a first curve denoting the target feeding profile, asecond curve denoting the baseline feeding profile with dynamicadjustments, and marking a zone indicative of the gap of feedingdeficiency formed between the target feeding profile and the baselinefeeding profile.
 12. The system of claim 3, further comprising a codefor automatically detecting location of a feeding tube within a targetfeeding zone, and triggering the automatic enteral feeding by theautomated enteral feeding device in response to the detected location ofthe feeding tube at the target feeding zone.
 13. The system of claim 3,wherein the target nutritional goal is computed match a resting energyexpenditure (REE) of the patient within a tolerance range, the REEcomputed based on CO₂ production rate and/or O₂ consumption rateestimates of the patient made by measurements of at least one sensor.14. The system of claim 3, wherein the target nutritional goal excludeshidden calories in medications prescribed to the patient.
 15. The systemof claim 3, further comprising a code for presenting an interactive GUIfor selection of at least one of a plurality of available feedingscompositions currently in stock that most closely match the targetnutritional goal.
 16. The system of claim 3, wherein the target feedingprofile is computed per respective time interval of a plurality of timeintervals as an increasing percentage of a maximal value of the targetnutritional goal at the end of each respective time interval, wherein ateach subsequent time interval the percentage is increased until themaximal value is met, wherein the target nutritional goal is set to themaximal value for additional subsequent time intervals.
 17. The systemof claim 3, further comprising a code for presenting within aninteractive GUI, a dashboard presenting an indication of deviation froma target zone of a real time value of at least one of: urine output,metabolism, gastric reflux event, REE used to compute the target feedingprofile, and gastro residual volume (GRV).
 18. A method of automatedenteral feeding of a patient, comprising: computing a target nutritionalgoal to reach at an end of a time interval based on a basal metabolicrate of a patient obtained over a portion of the time interval, whereinthe target nutritional goal comprises a volume to be delivered (VTBD) bythe end of the time interval corresponding to a target amount of energyexpenditure of the patient over the time interval computed according tothe basal metabolic rate; computing a target feeding profile defining atarget feeding rate for enteral feeding of the patient by an automatedenteral feeding device for reaching the VTBD by the end of the timeinterval; continuously monitoring the real time energy expenditure ofthe patient over the time interval based on continuous measurements ofthe basal metabolic rate; dynamically adapting the target nutritionalgoal and corresponding VTBD to compute a maximum VTBD (max VTBD) toreach at the end of the time interval according to dynamic adaptationsof the monitored real time energy expenditure of the patient;dynamically adapting the target feeding rate and the correspondingtarget feeding profile for a remaining portion of the time interval forreaching the max VTBD by the end of the time interval, wherein thetarget feeding profile tracks changes of the basal metabolic rate.